Systems and methods for expression-based classification of thyroid tissue

ABSTRACT

A system for classifying thyroid nodule tissue as malignant or benign is provided that is based on the identification of sets of gene transcripts, which are characterized in that changes in expression of each gene transcript within a set of gene transcripts can be correlated to with either malignant or benign thyroid nodule disease. The thyroid classification system provides for sets of “thyroid classifying” target sequences and further provides for combinations of polynucleotide probes and primers derived there from. These combinations of polynucleotide probes can be provided in solution or as an array. The combination of probes and the arrays can be used for diagnosis. The invention further provides further methods of classifying thyroid nodule tissue.

FIELD OF THE INVENTION

This invention relates to the field of diagnostics and in particular to systems and methods for diagnosis of thyroid cancer.

BACKGROUND

Thyroid nodule disease is a common clinical problem, found in 4-7% of the living adult population in North America. The occurrence of thyroid nodules increases with age; autopsies reveal the presence of thyroid nodules in 50% of the population. It is estimated that, at 80 years old, 90% of the population will have at least one thyroid nodule. However, the vast majority of solitary thyroid nodules are benign in nature, and would require no further treatment if a correct diagnosis could be obtained without surgery.

A number of techniques can be used to diagnose thyroid conditions, including radioactive thyroid scans, ultrasound, thyroid hormone level and thyroglobulin measurements, and fine needle aspiration biopsy (FNAB). Thyroid scans do not effectively distinguish benign and malignant conditions, however, and are typically used in conjunction with other techniques. Similarly, ultrasound may provide information suggestive of either benign or malignant conditions, but cannot definitively diagnose thyroid status. Measurements of thyroid hormone level and thyroglobulin can be informative, but are nondiagnostic by themselves.

Thyroid FNAB is the only non-surgical method which can by itself differentiate malignant and benign nodules. More than 300,000 fine needle aspiration biopsies (FNAB) of the thyroid are performed annually in the US and evaluated using cytology. The primary purpose of FNAB is to distinguish thyroid nodules that require immediate surgical intervention (e.g., total thyroidectomy in the case of a diagnosis of malignant disease) from nodules that can be treated effectively with less aggressive clinical approaches.

In FNAB, samples of thyroid cells are obtained by inserting a needle into the thyroid and aspirating cells into a syringe. Usually, 2 to 4 aspirations are made from different sites in each nodule. The cells are mounted on a slide (for each aspiration, 2 to 4 slides are prepared), stained, and examined. The sample is then classified as nondiagnostic (indeterminate), benign, suspicious or malignant. Most samples are categorized as benign.

FNAB can be used to successfully diagnose papillary carcinoma, medullary carcinoma, anaplastic carcinoma, thyroid lymphoma and metastases to the thyroid from other sites. Papillary carcinoma accounts for ˜60-70% and the follicular variant of papillary carcinoma accounts for ˜6% of thyroid cancers. These well differentiated thyroid cancers are usually curable, but they must be found first.

Especially problematic are cases considered ‘suspicious’, ‘inadequate’ or ‘indeterminate’ by cytological diagnosis of FNAB samples. These patients are invariably triaged by invasive surgery, which has a significant morbidity. Overtreatment with total-thyroidectomy frequently occurs as a result; it is estimated that less than 25% of patients with such diagnoses in fact have cancer that warrants removal of the thyroid gland. Approximately 5-10% of samples are classified as nondiagnostic by FNAB. In those cases, FNAB can be repeated; however, only half of repeat biopsies yield a diagnostic result. For the remaining patients, further testing and surgery may be required. Due to the fear of cancer, invasive surgery is chosen, but in most cases is unnecessary. Approximately 10-20% of samples are classified as suspicious by FNAB. Of these, approximately 25% will ultimately prove to be malignant after surgery, typically exhibiting follicular or Hurthle cell cancers, which cannot be diagnosed by FNAB. Follicular carcinoma, which accounts for ˜12-15% of all thyroid cancers and the less prevalent Hurthle cell carcinoma cannot be distinguished cytologically from benign follicular or Hurthle cell adenomas. Therefore, most patients with suspicious biopsies are typically subjected to surgery, when in fact ˜75% of these patients do not have malignant disease.

A contributing factor to the difficulties with current FNAB cytology-based diagnoses is the variability between different pathologists and cytopathologists in diagnostic agreement between cytological analysis and final histological review, ranging from 40%-90%. The overall accuracy of diagnoses using only FNAB ranges from 60% to >90%, and is dependent on the expertise of the cytologist and whether or not suspicious' or ‘indeterminate’ diagnostic categories are included in the reported accuracy of the study (see http://www.endocrineweb.com/noduleus.html). When factoring the cytology diagnostic categories of ‘suspicious’ or ‘indeterminate’, the literature shows that the overall specificity of FNAB cytology for diagnosis of malignant disease decreases dramatically to <60% with false-positive rates of ˜40%. Patients with malignant thyroid disease are invariably treated by total removal of the tumor and all of the thyroid gland followed by radioactive iodine treatment, whereas benign thyroid disease can be treated less aggressively with a near-total thyroidectomy, partial thyroidectomy (e.g., ‘lobectomy’) or a watchful-waiting approach (e.g., observation without surgical intervention). As FNAB and cytology cannot reliably distinguish malignant from benign disease in cases with ‘suspicious’ cytological findings, such as occurs in the case of follicular and Hurthle cell lesions, these patients are typically all treated as if they were diagnosed with malignant disease (i.e., with aggressive surgery). Since only a small fraction of these patients in fact have malignant disease, over-treatment of thyroid nodule disease patients occurs frequently, with significant consequences for patients. As such, many unnecessary thyroidectomies are therefore performed in patients with what ultimately proves to be benign or non-neoplastic thyroid nodule disease when an FNAB sample is deemed as ‘suspicious’ or ‘indeterminate.’ These deficiencies negatively impact patient outcomes, long-term well-being and healthcare efficiencies.

Use of molecular analyses has the potential to increase the sensitivity, specificity and/or overall accuracy of thyroid diagnoses as compared to FNAB cytology alone. Such a result would reduce the number of unnecessary surgeries for patients without malignant disease and avoid inadvertent undertreatment of highly curable thyroid cancers resulting from misdiagnoses. However, prior attempts at using gene expression profiling to develop diagnostic gene expression signatures and identify mRNA biomarkers useful for the differential diagnosis of thyroid nodule disease have not yet yielded new clinical tools to improve the diagnosis of malignant from benign thyroid nodule disease from clinical specimens. Most of these efforts and those of protein immunohistochemistry studies focused on the protein-encoding genome. However, the transcriptome is inherently more complex than this, given that <2% of the genome encodes for protein and recent studies that have shown that more than 90% of the genome undergoes transcription yielding millions of non-coding RNA transcripts that serve regulatory roles over the protein-encoding transcriptome. So, gene-level analysis may provide only a rough estimate of diagnosis as it cannot capture the full differences between the genomes of malignant and benign thyroid nodule disease (e.g., alternative gene splicing, non-coding and functional RNA expression). Recent efforts to validate a 3-gene signature for diagnosis of thyroid nodule disease FNAB with a QRT-PCR approach report a low diagnostic accuracy in a large validation study (see Sibru et al., citation #14). Other prior attempts using gene-biased microarrays showed similar performance characteristics with low diagnostic accuracy for gene-based signatures (see Jiang et al., US 2007/0037186 A1). For example, Jiang et al., (US 2007/0037186 A1) disclosed a 4-gene QRT-PCR panel with a sensitivity of 92% but a specificity of just 61%. In addition, since these signatures were developed and validated from fresh or fresh frozen tissue specimens their applicability to more routine tissues specimens commonly available in the clinic such as formalin-fixed paraffin embedded fine-needle aspirate cell blocks and surgical resections is not assured because of the impact of sample processing on nucleic acid integrity. As a result, diagnoses using these provide results little better than currently available FNAB cytology in widespread clinical use.

This background information is provided for the purpose of making known information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.

SUMMARY OF THE INVENTION

An object of the present invention is to provide systems and methods for expression-based classification of thyroid tissue. In accordance with one aspect of the present invention, there is provided a system for expression-based classification of thyroid tissue as malignant or benign, said system comprising one or more polynucleotides, each of said polynucleotides capable of specifically hybridizing to a RNA transcript of a gene selected from the group of genes set forth in Table 1.

In accordance with another aspect of the present invention, there is provided a system for expression-based classification of thyroid tissue as malignant or benign, said system comprising a set of polynucleotides for detecting a set of target sequences selected from the group consisting of SEQ ID NOs: 1-7; SEQ ID NOs: 1-12 and 283-306; and SEQ ID NOs: 261, 657, 658, and 659.

In accordance with another aspect of the present invention, there is provided a nucleic acid array for expression-based classification of thyroid tissue as malignant or benign, said array comprising at least ten probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of a gene selected from the group of genes set forth in Table 1, or a portion of said transcript.

In accordance with another aspect of the present invention, there is provided a method of classifying a thyroid nodule in a subject as malignant or benign, said method comprising: (a) determining the expression level of one or more transcripts of one or more genes in a test sample obtained from said subject to provide an expression pattern profile, said one or more genes selected from the group of genes set forth in Table 1, and (c) comparing said expression pattern profile with a reference expression pattern profile.

In accordance with another aspect of the present invention, there is provided a kit for characterizing the expression of one or more nucleic acid sequences depicted in SEQ ID NOs: 1-659 comprising one or more nucleic acids selected from:

-   -   (a) a nucleic acid depicted in any of SEQ ID NOs: 1-659;     -   (b) an RNA form of any of the nucleic acids depicted in SEQ ID         NOs: 1-659;     -   (c) a peptide nucleic acid form of any of the nucleic acids         depicted in SEQ ID NOs: 1-659;     -   (d) a nucleic acid comprising at least 20 consecutive bases of         any of (a-c);     -   (e) a nucleic acid comprising at least 25 consecutive bases         having at least 90% sequence identity to any of (a-c); or     -   (f) a complement to any of (a-e); and     -   optionally instructions for correlating the expression level of         said one or more nucleic acid sequences with the disease state         of thyroid tissue.

In accordance with another aspect of the present invention, there is provided an array of probe nucleic acids certified for use in classifying thyroid disease status, wherein said array comprises at least two different probe nucleic acids that specifically hybridize to corresponding different target nucleic acids depicted in one of SEQ ID NOs: 1-659, an RNA form thereof, or a complement to either thereof.

In accordance with another aspect of the present invention, there is provided a device for classifying a biological sample from a thyroid gland as malignant or benign, the device comprising means for measuring the expression level of one or more transcripts of one or more genes selected from the group of genes set forth in Table 1; means for correlating the expression level with a classification of thyroid disease status; and means for outputting the thyroid disease status.

In accordance with another aspect of the present invention, there is provided a computer-readable medium comprising one or more digitally-encoded expression pattern profiles representative of the level of expression of one or more transcripts of one or more genes selected from the group of genes set forth in Table 1, each of said one or more expression pattern profiles being associated with a value wherein each of said values is correlated with the presence of malignant or benign tissue in a thyroid gland sample.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the invention will become more apparent in the following detailed description in which reference is made to the appended drawings.

FIG. 1 is a pie chart that depicts the annotations of the 656 selected RNAs from genes previously described in the literature that were identified as differentially expressed in the training subset. Note that more than 60% correspond to non-canonical expressed transcripts (i.e., intronic, antisense and extra-genic) while only a small portion are actually from exonic sequences of protein-encoding genes. The labels in the pie chart indicate the position of the differentially expressed probes relative to the nearest annotated gene. CDS-RNA overlaps with the coding sequence translations of mRNAs in RefSeq or GenBank databases, NC-‘non-coding’ RNA does not overlap with the CDS.

FIG. 2 depicts mean POP scores for pathological diagnostic categories of malignant and benign patient specimens in the three subsets of patient specimens evaluated. ‘POP’ (patient outcome predictor) scores for thyroid nodule disease benign and malignant sample groups were derived using 7- and 36-RNA target sequence metagenes to generate patient outcome predictor scores normalized on a data range of 0-100 points. Differences in POP scores between benign and malignant thyroid nodule disease groups were highly significant as evaluated by t-tests for significance (p<1E-6). Error bars indicate the standard error of the mean.

FIG. 3 shows the correlation between POP scores generated using the 7- and 36-RNA metagenes in analysis of pre-operative and post-operative specimens for a subset of eight patients. The correlation coefficient R² value is 0.88 for both of target sequence metagenes.

FIG. 4 depicts receiver-operator curves (ROC) of the POP scores derived from the 7-RNA metagene. The area-under-the curve is indicated below the ROC curve and show that the POP scores as a continuous variable (i.e., without setting an arbitrary cut-points) are excellent discriminators of malignant and benign disease.

FIG. 5 depicts receiver-operator curves (ROC) of the POP scores derived from the 36-RNA metagene. The area-under-the curve is indicated below the ROC curve and show that the POP scores as a continuous variable (i.e., without setting an arbitrary cut-points) are excellent discriminators of malignant and benign disease.

FIG. 6 depicts mean SPOCK1 RNA metagene values for pathological diagnostic categories of malignant and benign patient specimens in the three subsets of patient specimens evaluated. SPOCK1 RNA metagene was generated by a weighted linear combination of expression values for exon 11, intron 2, 3 and 6 RNA target sequences. Error bars indicate the standard error of the mean.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a system and methods for classifying thyroid tissue from a subject as malignant or benign, which allows for the diagnosis of thyroid cancer in the subject. The system and methods are based on the identification of gene transcripts that are differentially expressed in thyroid cancer relative to benign thyroid nodule disease. These gene transcripts can be considered as a library which can be used as a resource for the identification of sets of specific target sequences (“thyroid classification sets”), which may represent the entire library of gene transcripts or a subset of the library and the detection of which is indicative of the status of the thyroid tissue (for example, malignant or benign). The invention further provides for probes capable of detecting these target sequences and primers that are capable of amplifying the target sequences.

In accordance with one embodiment of the invention, the target sequences comprised by the thyroid classification set are sequences based on or derived from the gene transcripts from the library, or a subset thereof. Such sequences are occasionally referred to herein as “probe selection regions” or “PSRs.” In another embodiment of the invention, the target sequences comprised by the thyroid classification set are sequences based on the gene transcripts from the library, or a subset thereof, and include both coding and non-coding sequences.

In one embodiment, the systems and methods provide for the molecular analysis of the expression levels of one or more of the target sequences as set forth in SEQ ID NOs: 1-659 (Table 3). Increased relative expression of one or more target sequences in Group I corresponding to the sequences as set forth in SEQ ID NOs: 1-4, and 8-282, and/or decreased relative expression of one or more target sequences in Group II corresponding to the sequences as set forth in SEQ ID NOs: 5-7, and 283-659, can be correlated with increased likelihood of malignant thyroid nodule disease. Conversely, increased relative expression of one or more target sequences in Group II and/or decreased relative expression of one or more target sequences in Group I can be correlated with an increased likelihood of benign thyroid nodule disease. Subsets and combinations of these target sequences or probes complementary thereto may be used as described herein.

Before the present invention is described in further detail, it is to be understood that this invention is not limited to the particular methodology, compositions, articles or machines described, as such methods, compositions, articles or machines can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.

DEFINITIONS

Unless defined otherwise or the context clearly dictates otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In describing the present invention, the following terms will be employed, and are intended to be defined as indicated below.

The term “polynucleotide” as used herein refers to a polymer of greater than one nucleotide in length of ribonucleic acid (RNA), deoxyribonucleic acid (DNA), hybrid RNA/DNA, modified RNA or DNA, or RNA or DNA mimetics, including peptide nucleic acids (PNAs). The polynucleotides may be single- or double-stranded. The term includes polynucleotides composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotides having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotides are well-known in the art and for the purposes of the present invention, are referred to as “analogues.”

“Complementary” or “substantially complementary” refers to the ability to hybridize or base pair between nucleotides or nucleic acids, such as, for instance, between a sensor peptide nucleic acid or polynucleotide and a target polynucleotide. Complementary nucleotides are, generally, A and T (or A and U), or C and G. Two single-stranded polynucleotides or PNAs are said to be substantially complementary when the bases of one strand, optimally aligned and compared and with appropriate insertions or deletions, pair with at least about 80% of the bases of the other strand, usually at least about 90% to 95%, and more preferably from about 98 to 100%.

Alternatively, substantial complementarity exists when a polynucleotide will hybridize under selective hybridization conditions to its complement. Typically, selective hybridization will occur when there is at least about 65% complementarity over a stretch of at least 14 to 25 bases, for example at least about 75%, or at least about 90% complementarity. See, M. Kanehisa Nucleic Acids Res. 12:203 (1984).

“Preferential binding” or “preferential hybridization” refers to the increased propensity of one polynucleotide to bind to its complement in a sample as compared to a noncomplementary polymer in the sample.

Hybridization conditions will typically include salt concentrations of less than about 1M, more usually less than about 500 mM, for example less than about 200 mM. In the case of hybridization between a peptide nucleic acid and a polynucleotide, the hybridization can be done in solutions containing little or no salt. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., and more typically greater than about 30° C., for example in excess of about 37° C. Longer fragments may require higher hybridization temperatures for specific hybridization as is known in the art. Other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, presence of organic solvents and extent of base mismatching, and the combination of parameters used is more important than the absolute measure of any one alone. Other hybridization conditions which may be controlled include buffer type and concentration, solution pH, presence and concentration of blocking reagents to decrease background binding such as repeat sequences or blocking protein solutions, detergent type(s) and concentrations, molecules such as polymers which increase the relative concentration of the polynucleotides, metal ion(s) and their concentration(s), chelator(s) and their concentrations, and other conditions known in the art.

“Multiplexing” herein refers to an assay or other analytical method in which multiple analytes can be assayed simultaneously.

A “target sequence” as used herein (also occasionally referred to as a “PSR” or “probe selection region”) refers to a region of the genome against which one or more probes can be designed. As used herein, a probe is any polynucleotide capable of selectively hybridizing to a target sequence or its complement, or to an RNA version of either. A probe may comprise ribonucleotides, deoxyribonucleotides, peptide nucleic acids, and combinations thereof. A probe may optionally comprise one or more labels. In some embodiments, a probe may be used to amplify one or both strands of a target sequence or an RNA form thereof, acting as a sole primer in an amplification reaction or as a member of a set of primers.

“Having” is an open ended phrase like “comprising” and “including,” and includes circumstances where additional elements are included and circumstances where they are not.

“Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event or circumstance occurs and instances in which it does not.

The term “suspected of comprising thyroid cancer,” as used in reference to biological samples or purified fractions or components thereof or products derived therefrom, refers to any sample or product that is to analyzed for the expression of the target sequences described herein, and includes samples comprising normal thyroid tissue, as well as samples comprising thyroid tumors, whether benign or malignant. Such tissue may be obtained from the thyroid itself, from another location within a patient that is a suspected metastases, or from a known sample of malignant thyroid cancer or from a known thyroid cancer cell line. Samples known to be malignant can function as positive controls, while samples known to be noncancerous (or of nonthyroid origin) can function as negative controls, but are “suspected” of comprising thyroid cancer in that they are tested to determine whether the assay being performed produces false positives or other abnormal results, indicating a problem with a given assay.

As used herein, the term “about” refers to approximately a +/−10% variation from a given value. It is to be understood that such a variation is always included in any given value provided herein, whether or not it is specifically referred to.

Use of the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, reference to “a polynucleotide” includes a plurality of polynucleotides, reference to “a target” includes a plurality of such targets, reference to “a normalization method” includes a plurality of such methods, and the like. Additionally, use of specific plural references, such as “two,” “three,” etc., read on larger numbers of the same subject, unless the context clearly dictates otherwise.

Terms such as “connected,” “attached,” “linked” and “conjugated” are used interchangeably herein and encompass direct as well as indirect connection, attachment, linkage or conjugation unless the context clearly dictates otherwise.

Where a range of values is recited, it is to be understood that each intervening integer value, and each fraction thereof, between the recited upper and lower limits of that range is also specifically disclosed, along with each subrange between such values. The upper and lower limits of any range can independently be included in or excluded from the range, and each range where either, neither or both limits are included is also encompassed within the invention. Where a value being discussed has inherent limits, for example where a component can be present at a concentration of from 0 to 100%, or where the pH of an aqueous solution can range from 1 to 14, those inherent limits are specifically disclosed. Where a value is explicitly recited, it is to be understood that values which are about the same quantity or amount as the recited value are also within the scope of the invention, as are ranges based thereon. Where a combination is disclosed, each subcombination of the elements of that combination is also specifically disclosed and is within the scope of the invention. Conversely, where different elements or groups of elements are disclosed, combinations thereof are also disclosed. Where any element of an invention is disclosed as having a plurality of alternatives, examples of that invention in which each alternative is excluded singly or in any combination with the other alternatives are also hereby disclosed; more than one element of an invention can have such exclusions, and all combinations of elements having such exclusions are hereby disclosed.

Thyroid Classification System

The system of the present invention is based on the identification of a library of gene transcripts that are differentially expressed in thyroid cancer relative to benign thyroid nodule disease and thus may be diagnostic for thyroid cancer. For example, relative over and/or under expression of one or more of the gene transcripts in a thyroid nodule sample compared to a reference sample or expression profile or signature there from may be indicative of a malignant condition. The reference sample can be, for example, from one or more benign thyroid nodules from one or more references subject(s). The reference expression profile or signature may optionally be normalized to one or more appropriate reference gene transcripts. Alternatively or in addition to, expression of one or more of the gene transcripts in a thyroid nodule sample may be compared to an expression profile or signature from one or more known thyroid cancer samples such that a substantially similar expression profile or signature may be used to validate a finding of cancer or may be compared to the expression profile or signature from normal thyroid tissue.

Expression profiles or signatures from diagnostic samples may be normalized to one or more house keeping gene transcripts such that normalized over and/or under expression of one or more of the gene transcripts in a thyroid nodule sample may be indicative of a malignant condition.

Thyroid Classification Library

The Thyroid Classification Library in accordance with the present invention comprises one or more gene transcripts whose relative and/or normalized expression is indicative of a thyroid malignancy or of benign thyroid nodule disease. Exemplary genes from which a transcribed RNA or encoded protein shows differential expression in benign and/or malignant thyroid tissue are shown in Table 1. A single gene may give rise to more than one gene transcript. Not all transcripts of a given gene are necessarily indicative of a thyroid malignancy or of benign thyroid nodule disease. A single gene may also give rise to two or more transcripts, where at least one of the two or more transcripts is indicative of a thyroid malignancy while at least one different transcript is indicative of benign thyroid disease. In one embodiment, the library comprises one or more of the gene transcripts of the genes shown in Table 1.

In one embodiment, the library comprises at least one transcript from at least one gene selected from those listed in Table 1. In one embodiment, the library comprises at least one transcript from each of at least 5 genes selected from those listed in Table 1. In another embodiment, the library comprises at least one transcript from each of at least 10 genes selected from those listed in Table 1. In a further embodiment, the library comprises at least one transcript from each of at least 15 genes selected from those listed in Table 1. In other embodiments, the library comprises at least one transcript from each of at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60 and at least 65 genes selected from those listed in Table 1. In a further embodiment, the library comprises at least one transcript from all of the genes listed in Table 1. In a further embodiment, the library comprises at all transcripts from all of the genes listed in Table 1.

In one embodiment, the library comprises at least one transcript from at least one gene selected from the group consisting of ABCA8 (ATP-binding cassette, sub-family A (ABC1), member 8); ADORA1 (Adenosine A1 receptor); ALOX5 (Arachidonate 5-lipoxygenase); AMIGO2 (Adhesion molecule with Ig-like domain 2); ANK2 (Ankyrin 2, neuronal); APOE (Apolipoprotein E); ARG2 (Arginase, type II); ARHGAP11A (Rho GTPase activating protein 11A); ARMCX3 (Armadillo repeat containing, X-linked 3); BCL2 (B-cell CLL/lymphoma 2); BEX1 (Brain expressed, X-linked 1); BMP8A (Bone morphogenetic protein 8a); C1orf212 (Chromosome 1 open reading frame 212); C7orf24 (Chromosome 7 open reading frame 24); CA4 (Carbonic anhydrase IV); CAMK2N1 (Calcium/calmodulin-dependent protein kinase II inhibitor 1); CCL14 (Chemokine (C—C motif) ligand 14); CCL21 (Chemokine (C—C motif) ligand 21); CCND1 (Cyclin D1); CD44 (CD44 molecule (Indian blood group)): CD55 (CD55 molecule, decay accelerating factor for complement (Cromer blood group)); CDH16 (Cadherin 16, KSP-cadherin); CDH3 (Cadherin 3, type 1, P-cadherin (placental)); CDKN2A (Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)); CFD (Complement factor D (adipsin)); ChGn (Chondroitin beta1,4 N-acetylgalactosaminyltransferase); CHI3L1 (Chitinase 3-like 1 (cartilage glycoprotein-39)); CHRDL1 (Chordin-like 1); CHST2 (Carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2); CITED1 (Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 1); CKS2 (CDC28 protein kinase regulatory subunit 2): COL9A3 (Collagen, type IX, alpha 3): CRABP1 (Cellular retinoic acid binding protein 1); CSNK1 G2 (Casein kinase 1, gamma 2); CST6 (Cystatin E/M); CTSC (Cathepsin C); CTSH (Cathepsin H); CTSS (Cathepsin S); DDIT3 (DNA-damage-inducible transcript 3); DIO1 (Deiodinase, iodothyronine, type I): DIO2 (Deiodinase, iodothyronine, type II): DLG7 (Discs, large homolog 7 (Drosophila)); DPP4 (Dipeptidyl-peptidase 4 (CD26, adenosine deaminase complexing protein 2)); DUSP5 (Dual specificity phosphatase 5); DUSP6 (Dual specificity phosphatase 6); EFEMP1 (EGF-containing fibulin-like extracellular matrix protein 1); ELMO1 (Engulfment and cell motility 1); ENDOD1 (Endonuclease domain containing 1); ENPP1 (Ectonucleotide pyrophosphatase/phosphodiesterase 1); EPS8 (Epidermal growth factor receptor pathway substrate 8); ETHE1 (Ethylmalonic encephalopathy 1); ETV5 (Ets variant gene 5 (ets-related molecule)); FABP4 (Fatty acid binding protein 4, adipocyte); FAM129A (Family with sequence similarity 129, member A); FAM129B (Family with sequence similarity 129, member B); FAM129C (Family with sequence similarity 129, member C); FBLN1 (Fibulin 1); FCGBP (Fc fragment of IgG binding protein); FHL1 (Four and a half LIM domains 1); FN1 (Fibronectin 1); FZD4 (Frizzled homolog 4 (Drosophila)); GABBR2 (Gamma-aminobutyric acid (GABA) B receptor, 2); GALE (UDP-galactose-4-epimerase); GALNT7 (UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 7 (GalNAc-T7)); GDF15 (Growth differentiation factor 15); GJB3 (Gap junction protein, beta 3, 31 kDa); GPM6A (Glycoprotein M6A); HBA2 (Hemoglobin, alpha 2); HBD (Hemoglobin, delta); HLA-DMB (Major histocompatibility complex, class II, DM beta); HLA-DQA1 (Major histocompatibility complex, class II, DQ alpha 1); HLA-DRA (Major histocompatibility complex, class II, DR alpha); HLF (Hepatic leukemia factor); HMGA2 (High mobility group AT-hook 2): ICAM1 (Intercellular adhesion molecule 1 (CD54), human rhinovirus receptor); ICAM4 (Intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)); ID4 (Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein); IGFBP6 (Insulin-like growth factor binding protein 6); IGSF1 (Immunoglobulin superfamily, member 1); ITM2A (Integral membrane protein 2A); ITPR1 (Inositol 1,4,5-triphosphate receptor, type 1); KCNAB1 (Potassium voltage-gated channel, shaker-related subfamily, beta member 1); KCNJ2 (Potassium inwardly-rectifying channel, subfamily J, member 2); KIAA0746 (KIAA0746 protein): KIT (V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog); KLK10 (Kallikrein-related peptidase 10); KRT15 (Keratin 15); KRT19 (Keratin 19); LAMB3 (Laminin, beta 3); LCN2 (Lipocalin 2); LGALS3 (Lectin, galactoside-binding, soluble, 3); LIPG (Lipase, endothelial); LPL (Lipoprotein lipase): LRP2 (Low density lipoprotein-related protein 2); LRP4 (Low density lipoprotein receptor-related protein 4); MATN2 (Matrilin 2); MET (Met proto-oncogene (hepatocyte growth factor receptor)); MPPED2 (Metallophosphoesterase domain containing 2); MPZL2 (Myelin protein zero-like 2); MPZL3 (Myelin protein zero-like 3); MRC2 (Mannose receptor, C type 2); MT1F (Metallothioncin 1F); MT1G (Metallothionein 1G); MT1H (Metallothionein 1H); MT1M (Metallothionein 1M); MT1X (Metallothionein 1X); MT2A (Metallothionein 2A); MTF1 (Metal-regulatory transcription factor 1); MUC1 (Mucin 1, cell surface associated); MYBPH (Myosin binding protein H); NELL2 (NEL-like 2 (chicken)); NMU (Neuromedin U); NRCAM (Neuronal cell adhesion molecule); NRIP1 (Nuclear receptor interacting protein 1); OPRM1 (Opioid receptor, mu 1); P4HA2 (Procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase), alpha polypeptide II); PDLIM4 (PDZ and LIM domain 4); PDZK1IP1 (PDZK1 interacting protein 1); PIP3-E (Phosphoinositide-binding protein PIP3-E); PLAU (Plasminogen activator, urokinase); PLXNC1 (Plexin C1); PPARGC1A (Peroxisome proliferator-activated receptor gamma, coactivator 1 alpha); PRIM2 (Primase, DNA, polypeptide 2 (58 kDa)); PRMT8 (Protein arginine methyltransferase 8); PROS1 (Protein S (alpha)); PRSS23 (Protease, serine, 23); PSD3 (Pleckstrin and Sec7 domain containing 3); PTPRE (Protein tyrosine phosphatase, receptor type, E); QPCT (Glutaminyl-peptide cyclotransferase (glutaminyl cyclase)); RAB23 (RAB23, member RAS oncogene family); RCN3 (Reticulocalbin 3, EF-hand calcium binding domain); RET (Ret proto-oncogene); RGS16 (Regulator of G-protein signaling 16): RHOBTB3 (Rho-related BTB domain containing 3); RXRG (Retinoid X receptor, gamma); S100A10 (S100 calcium binding protein A10); SCG5 (Secretogranin V (7B2 protein)); SDC4 (Syndecan 4); SERPINA1 (Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1); SFTPB (Surfactant, pulmonary-associated protein B); SLC1A1 (Solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, system Xag), member 1); SLC26A4 (Solute carrier family 26, member 4); SLC27A6 (Solute carrier family 27 (fatty acid transporter), member 6); SLC34A2 (Solute carrier family 34 (sodium phosphate), member 2): SLC5A5 (Solute carrier family 5 (sodium iodide symporter), member 5); SLP1 (Secretory leukocyte peptidase inhibitor); SOD3 (Superoxide dismutase 3, extracellular); SOX4 (SRY (sex determining region Y)-box 4); SPOCK1 (Sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1); SPP1 (Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1)); ST14 (Suppression of tumorigenicity 14 (colon carcinoma)); STT3A (STT3, subunit of the oligosaccharyltransferase complex, homolog A (S. cerevisiae)); STT3B (STT3, subunit of the oligosaccharyltransferase complex, homolog B (S. cerevisiae)); SYN1 (Synapsin I); TACSTD2 (Tumor-associated calcium signal transducer 2); TCEAL2 (Transcription elongation factor A (SII)-like 2); TFF3 (Trefoil factor 3 (intestinal)); TGFA (Transforming growth factor, alpha); TIAM1 (T-cell lymphoma invasion and metastasis 1); TIMP1 (TIMP metallopeptidase inhibitor 1); TM7SF4 (Transmembrane 7 superfamily member 4); TNC (Tenascin C (hexabrachion)); TPD52L1 (Tumor protein D52-like 1); TPO (Thyroid peroxidase); TRIM14 (Tripartite motif-containing 14); TUSC3 (Tumor suppressor candidate 3); VEGFA (Vascular endothelial growth factor A) and ZMAT4 (Zinc finger, matrin type 4).

In one embodiment, the library comprises at least one transcript from at least one gene selected from the group consisting of HMGA2 (High mobility group AT-hook 2); CDH3 (Cadherin 3, type 1, P-cadherin (placental)); SERPINA1 (Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1); IGFBP6 (Insulin-like growth factor binding protein 6); TPO (Thyroid peroxidase); KIT (V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog) and MPPED2 (Metallophosphoesterase domain containing 2).

In one embodiment, the library comprises at least one transcript from at least one gene selected from the group consisting of HMGA2 (High mobility group AT-hook 2); CDH3 (Cadherin 3, type 1, P-cadherin (placental)); SERPINA1 (Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1); IGFBP6 (Insulin-like growth factor binding protein 6); TPO (Thyroid peroxidase); KIT (V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog), FN1 (fibronectin 1), HLF (Hepatic leukemia factor), MT1F (Metallothionein 1F), MT1G (Metallothionein 1G), PDLIM4 (PDZ and LIM domain 4), PPARGC1A (Peroxisome proliferator-activated receptor gamma, coactivator 1 alpha), SLC26A4 (Solute carrier family 26, member 4), TFF3 (Trefoil factor 3 (intestinal)), TACSTD2 (Tumor-associated calcium signal transducer 2), ZMAT4 (Zinc finger, matrin type 4) and MPPED2 (Metallophosphoesterase domain containing 2).

In one embodiment, the library comprises at least one transcript from Sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1.

The invention also contemplates that alternative libraries may be designed that include transcripts of one or more of the genes in Table 1, together with additional gene transcripts that are identified as having differential expression in benign and/or malignant thyroid tissue. As is known in the art, the publication and sequence databases can be mined using a variety of search strategies to identify appropriate candidates for inclusion in the library. For example, currently available scientific and medical publication databases such as Medline, Current Contents, OMIM (online Mendelian inheritance in man), various Biological and Chemical Abstracts, Journal indexes, and the like can be searched using term or key-word searches, or by author, title, or other relevant search parameters. Many such databases are publicly available, and strategies and procedures for identifying publications and their contents, for example, genes, other nucleotide sequences, descriptions, indications, expression pattern, etc, are well known to those skilled in the art. Numerous databases are available through the internet for free or by subscription, see, for example, the National Center Biotechnology Information (NCBI), Infotrieve, Thomson ISI, and Science Magazine (published by the AAAS) websites. Additional or alternative publication or citation databases are also available that provide identical or similar types of information, any of which can be employed in the context of the invention. These databases can be searched for publications describing altered gene expression between malignant thyroid nodule disease and benign thyroid nodule disease. Additional potential candidate genes may be identified by searching the above described databases for differentially expressed proteins and by identifying the nucleotide sequence encoding the differentially expressed proteins.

Thyroid Classification Sets

A Thyroid Classification Set comprises one or more target sequences identified within the gene transcripts in the thyroid classification library, or a subset of these gene transcripts. The target sequences may be within the coding and/or non-coding regions of the gene transcripts. The set can comprise one or a plurality of target sequences from each gene transcript in the library, or subset thereof. The relative and/or normalized level of these target sequences in a sample is indicative of the level of expression of the particular gene transcript and thus of a thyroid malignancy or of benign thyroid nodule disease. For example, the relative and/or normalized expression level of one or more of the target sequences may be indicative of a thyroid malignancy while the relative and/or normalized expression level of one or more other target sequences may be indicative of benign thyroid nodule disease.

Accordingly, one embodiment of the present invention provides for a library or catalog of candidate target sequences derived from the transcripts (both coding and non-coding regions) of at least one gene suitable for classifying thyroid nodules as being malignant or benign. In a further embodiment, the library or catalog of candidate target sequences comprises target sequences derived from the transcripts of one or more of the genes set forth in Table 1. The library or catalog in affect provides a resource list of transcripts from which target sequences appropriate for inclusion in a thyroid classification set can be derived. In one embodiment, an individual thyroid classification set may comprise target sequences derived from the transcripts of one or more genes exhibiting a positive correlation with thyroid cancer. In one embodiment, an individual thyroid classification set may comprise target sequences derived from the transcripts of one or more genes exhibiting a negative correlation with thyroid cancer. In one embodiment, an individual Thyroid Classification Set may comprise target sequences derived from the transcripts of from two or more genes, wherein at least one gene has a transcript that exhibits a positive correlation with thyroid cancer and at least one gene has a transcript that exhibits a negative correlation.

In one embodiment, the Thyroid Classification Set comprises target sequences derived from the transcripts of at least one gene. In one embodiment, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 5 genes. In another embodiment, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 10 genes. In a further embodiment, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 15 genes. In other embodiments, the Thyroid Classification set comprises target sequences derived from the transcripts of at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60 and at least 65 genes.

Following the identification of candidate gene transcripts, appropriate target sequences can be identified by screening for target sequences that have been annotated to be associated with each specific gene locus from a number of annotation sources including GenBank, RefSeq, Ensembl, dbEST, GENSCAN, TWINSCAN, Exoniphy, Vega, microRNAs registry and others (see Affymetrix Exon Array design note).

As part of the target sequence selection process, target sequences can be optionally further evaluated for potential cross-hybridization against other putative transcribed sequences in the design (but not the entire genome) to identify only those target sequences that are predicted to uniquely hybridize to a single target.

The set of target sequences that are predicted to uniquely hybridize to a single target can be further filtered using a variety of criteria including, for example, sequence length, for their mean expression levels across a wide selection of human tissues, as being representative of transcripts expressed either as novel alternative (i.e., non-consensus) exons, alternative retained introns, novel exons 5′ or 3′ of the gene's transcriptional start site or representing transcripts expressed in a manner antisense to the gene, amongst others. Representative, non-limiting examples of filtered candidate target sequences in accordance with the present invention are shown in Table 3.

In one embodiment, the Thyroid Classification Set comprises target sequences derived from intron #3 of High mobility group AT-hook 2; exon #16 of Cadherin 3, type 1, P-cadherin (placental): exon #5 of Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1; exon #4 of Insulin-like growth factor binding protein 6; exon #17 of Thyroid peroxidase; exon #22 of V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog and intron #3 of Metallophosphoesterase domain containing 2.

In one embodiment, the Thyroid Classification Set comprises target sequences derived from exon #16 of Cadherin 3, type 1, P-cadherin (placental); intron #25 of Fibronectin 1; intron #31 of Fibronectin 1; intron #40 of Fibronectin 1; exon #1 of Hepatic leukemia factor; intron #3 of High mobility group AT-hook 2; exon #4 of Insulin-like growth factor binding protein 6; 25 base pairs 3′ of Metallophosphoesterase domain containing 2; exon #6 of Metallophosphoesterase domain containing 2; intron #1 of Metallophosphoesterase domain containing 2; intron #3 of Metallophosphoesterase domain containing 2; intron #3 of Metallophosphoesterase domain containing 2; intron #3 of Metallophosphoesterase domain containing 2; exon #3 of Metallothionein 1F; exon #3 of Metallothionein 1 G; exon #7 of PDZ and LIM domain 4; exon #5 of Peroxisome proliferator-activated receptor gamma, coactivator 1 alpha; exon #5 of Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1; exon #13 of Solute carrier family 26, member 4; exon #17 of Solute carrier family 26, member 4; exon #5 of Solute carrier family 26, member 4; exon #6 of Solute carrier family 26, member 4; 185 base pairs 3′ of Thyroid peroxidase; exon #1 of Thyroid peroxidase; exon #13 of Thyroid peroxidase; exon #17 of Thyroid peroxidase; exon #5 of Thyroid peroxidase; exon #5 of Thyroid peroxidase; exon #7 of Thyroid peroxidase; intron #15 of Thyroid peroxidase; intron #8 of Thyroid peroxidase; exon #3 of Trefoil factor 3 (intestinal): exon #1 of Tumor-associated calcium signal transducer 2; exon #22 of V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog; exon #6 of Zinc finger, matrin type 4 and exon #7 of Zinc finger, matrin type 4.

In one embodiment, the Thyroid Classification Set comprises target sequences derived from exon #11, intron's #2, 3, and 6 of Sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1.

In one embodiment, the potential set of target sequences can be filtered for their expression levels using the multi-tissue expression data made publicly available by Affymetrix at (http://www.affymetrix.com/support/technical/sample_data/exon_array_data.affx) such that probes with, for example, expression across numerous tissues or no expression in thyroid tissue can be excluded.

In one embodiment, the thyroid classification set can be specifically designed to be indicative of malignant thyroid cancer in general or alternatively be indicative of one or more individual types of thyroid cancer.

Validation of Target Sequences

Following in silico selection of target sequences or review of the literature for published target sequences, each target sequence suitable for use in the thyroid classification set may be validated to confirm differential relative or normalized expression in thyroid cancer or benign thyroid nodule disease. Validation methods are known in the art and include hybridization techniques such as microarray analysis or Northern blotting using appropriate controls, and may include one or more additional steps, such as reverse transcription, transcription, PCR, RT-PCR and the like. The validation of the target sequences using these methods is well within the abilities of a worker skilled in the art.

Minimal Expression Signature

In one embodiment, individual thyroid classification sets provide for at least a determination of a minimal expression signature, capable of distinguishing malignant from benign thyroid nodule disease. Means for determining the appropriate number of target sequences necessary to obtain a minimal expression signature are known in the art and include the Nearest Shrunken Centroids (NSC) method.

In this method (see US 20070031873), a standardized centroid is computed for each class. This is the average gene expression for each gene in each class divided by the within-class standard deviation for that gene. Nearest centroid classification takes the gene expression profile of a new sample, and compares it to each of these class centroids. The class whose centroid that it is closest to, in squared distance, is the predicted class for that new sample. Nearest shrunken centroid classification “shrinks” each of the class centroids toward the overall centroid for all classes by an amount called the threshold. This shrinkage consists of moving the centroid towards zero by threshold, setting it equal to zero if it hits zero. For example if threshold was 2.0, a centroid of 3.2 would be shrunk to 1.2, a centroid of −3.4 would be shrunk to −1.4, and a centroid of 1.2 would be shrunk to zero. After shrinking the centroids, the new sample is classified by the usual nearest centroid rule, but using the shrunken class centroids. This shrinkage can make the classifier more accurate by reducing the effect of noisy genes and provides an automatic gene selection. In particular, if a gene is shrunk to zero for all classes, then it is eliminated from the prediction rule. Alternatively, it may be set to zero for all classes except one, and it can be learned that the high or low expression for that gene characterizes that class. The user decides on the value to use for threshold. Typically one examines a number of different choices. To guide in this choice, PAM does K-fold cross-validation for a range of threshold values. The samples are divided up at random into K roughly equally sized parts. For each part in turn, the classifier is built on the other K−1 parts then tested on the remaining part. This is done for a range of threshold values, and the cross-validated misclassification error rate is reported for each threshold value. Typically, the user would choose the threshold value giving the minimum cross-validated misclassification error rate.

Alternatively, minimal expression signatures can be established through the use of optimization algorithms such as the mean variance algorithm widely used in establishing stock portfolios. This method is described in detail in US patent publication number 20030194734. Essentially, the method calls for the establishment of a set of inputs (stocks in financial applications, expression as measured by intensity here) that will optimize the return (e.g., signal that is generated) one receives for using it while minimizing the variability of the return. In other words, the method calls for the establishment of a set of inputs (e.g., expression as measured by intensity) that will optimize the signal while minimizing variability. Many commercial software programs are available to conduct such operations. “Wagner Associates Mean-Variance Optimization Application,” referred to as “Wagner Software” throughout this specification, is preferred. This software uses functions from the “Wagner Associates Mean-Variance Optimization Library” to determine an efficient frontier and optimal portfolios in the Markowitz sense is preferred. Use of this type of software requires that microarray data be transformed so that it can be treated as an input in the way stock return and risk measurements are used when the software is used for its intended financial analysis purposes.

The process of selecting a minimal expression signature can also include the application of heuristic rules. Preferably, such rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to output from the optimization method. For example, the mean variance method of portfolio selection can be applied to microarray data for a number of genes differentially expressed in subjects with cancer. Output from the method would be an optimized set of genes that could include some genes that are expressed in peripheral blood as well as in diseased tissue.

Other heuristic rules can be applied that are not necessarily related to the biology in question. For example, one can apply a rule that only a prescribed percentage of the portfolio can be represented by a particular gene or group of genes. Commercially available software such as the Wagner Software readily accommodates these types of heuristics. This can be useful, for example, when factors other than accuracy and precision (e.g., anticipated licensing fees) have an impact on the desirability of including one or more genes.

In one embodiment, the thyroid classification set for obtaining a minimal expression signature comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of target sequences shown to have a positive correlation with malignant thyroid disease, for example those depicted in SEQ ID NOs:1-4, and 8-282 or a subset thereof. In another embodiment, the thyroid classification set for obtaining a minimal expression signature comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of those target sequences shown to have a positive correlation with benign thyroid disease, for example those depicted in of SEQ ID NOs:5-7, and 283-659, or a subset thereof. In yet another embodiment, the thyroid classification set for obtaining a minimal expression signature comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of target sequences shown to have a positive or negative correlation with malignant thyroid disease, for example those depicted in SEQ ID NOs: 1-659 or a subset thereof.

In some embodiments, the thyroid classification set comprises target sequences for detecting expression products of SEQ IDs: 1-659. In some embodiments, the thyroid classification set comprises probes for detecting expression levels of sequences exhibiting positive and negative correlation with a disease status of interest are employed. For example, a combination useful for identifying a sample as exhibiting malignant or benign disease comprises at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of those target sequences shown to have a positive correlation with malignant thyroid disease, for example those depicted in SEQ ID NOs: 1-4, and 8-282 or a subset thereof; and at least one, two, three, four, five, six, eight, 10, 15, 20, 25 or more of those target sequences shown to have a positive correlation with benign thyroid disease, for example those depicted in of SEQ ID NOs: 5-7, and 283-659, or a subset thereof.

Exemplary subsets and combinations of interest also include the target sequences as set forth in SEQ ID NOs: 1-7; SEQ ID NOs: 1-12 and 283-306; and SEQ ID NOs: 261, 657, 658, and 659.

Exemplary subsets of interest include those described herein, including in the examples. Exemplary combinations of interest include those utilizing one or more of the sequences listed in Tables 3, 4 and 5. Of particular interest are those combinations utilizing at least one sequence exhibiting positive correlation with the trait of interest, as well as those combinations utilizing at least one sequence exhibiting negative correlation with the trait of interest. Also of interest are those combinations utilizing at least two, at least three, at least four, at least five or at least six of those sequences exhibiting such a positive correlation, in combination with at least two, at least three, at least four, at least five, or at least six of those sequences exhibiting such a negative correlation. Exemplary combinations include those utilizing at least one, two, three, four, five or six of the target sequences depicted in Tables 3, 4 and 5.

It is to be recognized that those sequences shown as having a positive correlation with malignant disease conversely also possess a negative correlation with benign disease. Correspondingly, those sequences shown as having a positive correlation with benign disease also possess a negative correlation with malignant disease.

The thyroid classification set can optionally include one or more target sequences specifically derived from the transcripts of one or more housekeeping genes and/or one or more internal control target sequences and/or one or more negative control target sequences. In one embodiment, these target sequences can, for example, be used to normalize expression data. Housekeeping genes from which target sequences for inclusion in a Thyroid Classification Set can be derived from are known in the art and include those genes in which are expressed at a constant level in normal, benign and malignant thyroid tissue.

The target sequences described herein may be used alone or in combination with each other or with other known or later identified disease markers.

Thyroid Classification Probes/Primers

The system of the present invention provides for combinations of polynucleotide probes that are capable of detecting the target sequences of the Thyroid classification sets. Individual polynucleotide probes comprise a nucleotide sequence derived from the nucleotide sequence of the target sequences or complementary sequences thereof. The nucleotide sequence of the polynucleotide probe is designed such that it corresponds to, or is complementary to the target sequences. The polynucleotide probe can specifically hybridize under either stringent or lowered stringency hybridization conditions to a region of the target sequences, to the complement thereof, or to a nucleic acid sequence (such as a cDNA) derived therefrom.

The selection of the polynucleotide probe sequences and determination of their uniqueness may be carried out in silico using techniques known in the art, for example, based on a BLASTN search of the polynucleotide sequence in question against gene sequence databases, such as the Human Genome Sequence, UniGene, dbEST or the non-redundant database at NCBI. In one embodiment of the invention, the polynucleotide probe is complementary to a region of a target mRNA derived from a PSR in the thyroid classification set. Computer programs can also be employed to select probe sequences that will not cross hybridize or will not hybridize non-specifically.

One skilled in the art will understand that the nucleotide sequence of the polynucleotide probe need not be identical to its target sequence in order to specifically hybridise thereto. The polynucleotide probes of the present invention, therefore, comprise a nucleotide sequence that is at least about 75% identical to a region of the target gene or mRNA. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 90% identical a region of the target gene or mRNA. In a further embodiment, the nucleotide sequence of the polynucleotide probe is at least about 95% identical to a region of the target gene or mRNA. Methods of determining sequence identity are known in the art and can be determined, for example, by using the BLASTN program of the University of Wisconsin Computer Group (GCG) software or provided on the NCBI website. The nucleotide sequence of the polynucleotide probes of the present invention may exhibit variability by differing (e.g. by nucleotide substitution, including transition or transversion) at one, two, three, four or more nucleotides from the sequence of the target gene.

Other criteria known in the art may be employed in the design of the polynucleotide probes of the present invention. For example, the probes can be designed to have <50% G content and/or between about 25% and about 70% G+C content. Strategies to optimize probe hybridization to the target nucleic acid sequence can also be included in the process of probe selection. Hybridization under particular pH, salt, and temperature conditions can be optimized by taking into account melting temperatures and by using empirical rules that correlate with desired hybridization behaviours. Computer models may be used for predicting the intensity and concentration-dependence of probe hybridization.

As is known in the art, in order to represent a unique sequence in the human genome, a probe should be at least 15 nucleotides in length. Accordingly, the polynucleotide probes of the present invention range in length from about 15 nucleotides to the full length of the PSR or target mRNA. In one embodiment of the invention, the polynucleotide probes are at least about 15 nucleotides in length. In another embodiment, the polynucleotide probes are at least about 20 nucleotides in length. In a further embodiment, the polynucleotide probes are at least about 25 nucleotides in length. In another embodiment, the polynucleotide probes are between about 15 nucleotides and about 500 nucleotides in length. In other embodiments, the polynucleotide probes are between about 15 nucleotides and about 450 nucleotides, about 15 nucleotides and about 400 nucleotides, about 15 nucleotides and about 350 nucleotides, about 15 nucleotides and about 300 nucleotides in length.

The polynucleotide probes of a thyroid classification set can comprise RNA, DNA, RNA or DNA mimetics, or combinations thereof, and can be single-stranded or double-stranded. Thus the polynucleotide probes can be composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotide probes having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotide probes may provide desirable properties such as, for example, enhanced affinity for a target gene and increased stability.

The system of the present invention further provides for primers and primer pairs capable of amplifying target sequences defined by the thyroid classification set, or fragments or subsequences or complements thereof. The nucleotide sequences of the thyroid classifying set may be provided in computer-readable media for in silico applications and as a basis for the design of appropriate primers for amplification of one or more target sequences of the thyroid classifying set.

Primers based on the nucleotide sequences of target sequences can be designed for use in amplification of the target sequences. For use in amplification reactions such as PCR, a pair of primers will be used. The exact composition of the primer sequences is not critical to the invention, but for most applications the primers will hybridize to specific sequences of the thyroid classification set under stringent conditions, particularly under conditions of high stringency, as known in the art. The pairs of primers are usually chosen so as to generate an amplification product of at least about 50 nucleotides, more usually at least about 100 nucleotides. Algorithms for the selection of primer sequences are generally known, and are available in commercial software packages. These primers may be used in standard quantitative or qualitative PCR-based assays to assess transcript expression levels of RNAs defined by the thyroid classification set. Alternatively, these primers may be used in combination with probes, such as molecular beacons in amplifications using real-time PCR.

In one embodiment, the primers or primer pairs, when used in an amplification reaction, specifically amplify at least a portion of a nucleic acid depicted in one of SEQ ID NOs: 1-659, an RNA form thereof, or a complement to either thereof. Optionally, when amplified, either stand produced by amplification may be provided in purified and/or isolated form.

As is known in the art, a nucleoside is a base-sugar combination and a nucleotide is a nucleoside that further includes a phosphate group covalently linked to the sugar portion of the nucleoside. In forming oligonucleotides, the phosphate groups covalently link adjacent nucleosides to one another to form a linear polymeric compound, with the normal linkage or backbone of RNA and DNA being a 3′ to 5′ phosphodiester linkage. Specific examples of polynucleotide probes or primers useful in this invention include oligonucleotides containing modified backbones or non-natural internucleoside linkages. As defined in this specification, oligonucleotides having modified backbones include both those that retain a phosphorus atom in the backbone and those that lack a phosphorus atom in the backbone. For the purposes of the present invention, and as sometimes referenced in the art, modified oligonucleotides that do not have a phosphorus atom in their internucleoside backbone can also be considered to be oligonucleotides.

Exemplary polynucleotide probes or primers having modified oligonucleotide backbones include, for example, those with one or more modified internucleotide linkages that are phosphorothioates, chiral phosphorothioates, phosphorodithioates, phosphotriesters, aminoalkylphosphotriesters, methyl and other alkyl phosphonates including 3′-alkylene phosphonates and chiral phosphonates, phosphinates, phosphoramidates including 3′amino phosphoramidate and aminoalkylphosphoramidates, thionophosphoramidates, thionoalkyl-phosphonates, thionoalkylphosphotriesters, and boranophosphates having normal 3′-5′ linkages, 2′-5′ linked analogs of these, and those having inverted polarity wherein the adjacent pairs of nucleoside units are linked 3′-5′ to 5′-3′ or 2′-5′ to 5′-2′. Various salts, mixed salts and free acid forms are also included.

Exemplary modified oligonucleotide backbones that do not include a phosphorus atom are formed by short chain alkyl or cycloalkyl internucleoside linkages, mixed heteroatom and alkyl or cycloalkyl internucleoside linkages, or one or more short chain heteroatomic or heterocyclic internucleoside linkages. Such backbones include morpholino linkages (formed in part from the sugar portion of a nucleoside); siloxane backbones; sulfide, sulfoxide and sulphone backbones; formacetyl and thioformacetyl backbones; methylene formacetyl and thioformacetyl backbones; alkene containing backbones; sulphamate backbones; methyleneimino and methylenehydrazino backbones; sulphonate and sulfonamide backbones; amide backbones; and others having mixed N, O, S and CH₂ component parts.

The present invention also contemplates oligonucleotide mimetics in which both the sugar and the internucleoside linkage of the nucleotide units are replaced with novel groups. The base units are maintained for hybridization with an appropriate nucleic acid target compound. An example of such an oligonucleotide mimetic, which has been shown to have excellent hybridization properties, is a peptide nucleic acid (PNA) [Nielsen et al., Science, 254:1497-1500 (1991)]. In PNA compounds, the sugar-backbone of an oligonucleotide is replaced with an amide containing backbone, in particular an aminoethylglycine backbone. The nucleobases are retained and are bound directly or indirectly to aza-nitrogen atoms of the amide portion of the backbone.

The present invention also contemplates polynucleotide probes or primers comprising “locked nucleic acids” (LNAs), which are novel conformationally restricted oligonucleotide analogues containing a methylene bridge that connects the 2′-O of ribose with the 4′-C (see, Singh et al., Chem. Commun., 1998, 4:455-456). LNA and LNA analogues display very high duplex thermal stabilities with complementary DNA and RNA, stability towards 3′-exonuclease degradation, and good solubility properties. Synthesis of the LNA analogues of adenine, cytosine, guanine, 5-methylcytosine, thymine and uracil, their oligomerization, and nucleic acid recognition properties have been described (see Koshkin et al., Tetrahedron, 1998, 54:3607-3630). Studies of mis-matched sequences show that LNA obey the Watson-Crick base pairing rules with generally improved selectivity compared to the corresponding unmodified reference strands.

LNAs form duplexes with complementary DNA or RNA or with complementary LNA, with high thermal affinities. The universality of LNA-mediated hybridization has been emphasized by the formation of exceedingly stable LNA:LNA duplexes (Koshkin et al., J. Am. Chem. Soc., 1998, 120:13252-13253). LNA:LNA hybridization was shown to be the most thermally stable nucleic acid type duplex system, and the RNA-mimicking character of LNA was established at the duplex level. Introduction of three LNA monomers (T or A) resulted in significantly increased melting points toward DNA complements.

Synthesis of 2′-amino-LNA (Singh et al., J. Org. Chem., 1998, 63, 10035-10039) and 2′-methylamino-LNA has been described and thermal stability of their duplexes with complementary RNA and DNA strands reported. Preparation of phosphorothioate-LNA and 2′-thio-LNA have also been described (Kumar et al., Bioorg. Med. Chem. Lett., 1998, 8:2219-2222).

Modified polynucleotide probes or primers may also contain one or more substituted sugar moieties. For example, oligonucleotides may comprise sugars with one of the following substituents at the 2′ position: OH; F; O-, S-, or N-alkyl; O-, S-, or N-alkenyl: O-, S- or N-alkynyl: or O-alkyl-O-alkyl, wherein the alkyl, alkenyl and alkynyl may be substituted or unsubstituted C₁ to C₁₀ alkyl or C₂ to C₁₀ alkenyl and alkynyl. Examples of such groups are: O[(CH₂)_(n)O]_(m)CH₃, O(CH₂)_(n)OCH₃, O(CH₂)_(n) NH₂, O(CH₂)_(n)CH₃, O(CH₂)_(n)ONH₂, and O(CH₂)_(n)ON[(CH₂)_(n)CH₃)]₂, where n and m are from 1 to about 10. Alternatively, the oligonucleotides may comprise one of the following substituents at the 2′ position: C₁ to C₁₀ lower alkyl, substituted lower alkyl, alkaryl, aralkyl, O-alkaryl or O-aralkyl, SH, SCH₃, OCN, Cl, Br, CN, CF₃, OCF₃, SOCH₃, SO₂CH₃, ONO₂, NO₂, N₃, NH₂, heterocycloalkyl, heterocycloalkaryl, aminoalkylamino, polyalkylamino, substituted silyl, an RNA cleaving group, a reporter group, an intercalator, a group for improving the pharmacokinetic properties of an oligonucleotide, or a group for improving the pharmacodynamic properties of an oligonucleotide, and other substituents having similar properties. Specific examples include 2′-methoxyethoxy (2′-O—CH₂CH₂OCH₃, also known as 2′-O-(2-methoxyethyl) or 2′-MOE) [Martin et al., Helv. Chim. Acta, 78:486-504(1995)], 2′-dimethylaminooxyethoxy (O(CH₂)₂ON(CH₃)₂ group, also known as 2′-DMAOE), 2′-methoxy (2′-O—CH₃), 2′-aminopropoxy (2′-OCH₂CH₂CH₂NH₂) and 2′-fluoro (2′-F).

Similar modifications may also be made at other positions on the polynucleotide probes or primers, particularly the 3′ position of the sugar on the 3′ terminal nucleotide or in 2′-5′ linked oligonucleotides and the 5′ position of 5′ terminal nucleotide. Polynucleotide probes or primers may also have sugar mimetics such as cyclobutyl moieties in place of the pentofuranosyl sugar.

Polynucleotide probes or primers may also include modifications or substitutions to the nucleobase. As used herein, “unmodified” or “natural” nucleobases include the purine bases adenine (A) and guanine (G), and the pyrimidine bases thymine (T), cytosine (C) and uracil (U). Modified nucleobases include other synthetic and natural nucleobases such as 5-methylcytosine (5-me-C), 5-hydroxymethyl cytosine, xanthine, hypoxanthine, 2-aminoadenine, 6-methyl and other alkyl derivatives of adenine and guanine, 2-propyl and other alkyl derivatives of adenine and guanine, 2-thiouracil, 2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl uracil and cytosine, 6-azo uracil, cytosine and thymine, 5-uracil (pseudouracil), 4-thiouracil, 8-halo, 8-amino, 8-thiol, 8-thioalkyl, 8-hydroxyl and other 8-substituted adenines and guanines, 5-halo particularly 5-bromo, 5-trifluoromethyl and other 5-substituted uracils and cytosines, 7-methylguanine and 7-methyladenine, 8-azaguanine and 8-azaadenine, 7-deazaguanine and 7-deazaadenine and 3-deazaguanine and 3-deazaadenine. Further nucleobases include those disclosed in U.S. Pat. No. 3,687,808: The Concise Encyclopedia Of Polymer Science And Engineering, (1990) pp 858-859, Kroschwitz, J. I., ed. John Wiley & Sons; Englisch et al., Angewandte Chemie, Int. Ed., 30:613 (1991); and Sanghvi, Y. S., (1993) Antisense Research and Applications, pp 289-302, Crooke, S. T. and Lebleu, B., ed., CRC Press. Certain of these nucleobases are particularly useful for increasing the binding affinity of the polynucleotide probes of the invention. These include 5-substituted pyrimidines, 6-azapyrimidines and N-2, N-6 and O-6 substituted purines, including 2-aminopropyladenine, 5-propynyluracil and 5-propynylcytosine. 5-methylcytosine substitutions have been shown to increase nucleic acid duplex stability by 0.6-1.2° C. [Sanghvi, Y. S., (1993) Antisense Research and Applications, pp 276-278, Crooke, S. T. and Lebleu, B., ed., CRC Press, Boca Raton].

One skilled in the art will recognize that it is not necessary for all positions in a given polynucleotide probe or primer to be uniformly modified. The present invention, therefore, contemplates the incorporation of more than one of the aforementioned modifications into a single polynucleotide probe or even at a single nucleoside within the probe or primer.

One skilled in the art will also appreciate that the nucleotide sequence of the entire length of the polynucleotide probe or primer does not need to be derived from the target sequence. Thus, for example, the polynucleotide probe may comprise nucleotide sequences at the 5′ and/or 3′ to the transcription start and stop sites, respectively that are not derived from the target sequences. Nucleotide sequences which are not derived from the nucleotide sequence of the target sequence may provide additional functionality to the polynucleotide probe. For example, they may provide a restriction enzyme recognition sequence or a “tag” that facilitates detection, isolation, purification or immobilisation onto a solid support. Alternatively, the additional nucleotides may provide a self-complementary sequence that allows the primer/probe to adopt a hairpin configuration. Such configurations are necessary for certain probes, for example, molecular beacon and Scorpion probes, which can be used in solution hybridization techniques.

The polynucleotide probes or primers can incorporate moieties useful in detection, isolation, purification, or immobilisation, if desired. Such moieties are well-known in the art (see, for example, Ausubel et al., (1997 & updates) Current Protocols in Molecular Biology, Wiley & Sons, New York) and are chosen such that the ability of the probe to hybridize with its target sequence is not affected.

Examples of suitable moieties are detectable labels, such as radioisotopes, fluorophores, chemiluminophores, enzymes, colloidal particles, and fluorescent microparticles, as well as antigens, antibodies, haptens, avidin/streptavidin, biotin, haptens, enzyme cofactors/substrates, enzymes, and the like.

A label can optionally be attached to or incorporated into a probe or primer polynucleotide to allow detection and/or quantitation of a target polynucleotide representing the target sequence of interest. The target polynucleotide may be the expressed target sequence RNA itself, a cDNA copy thereof, or an amplification product derived therefrom, and may be the positive or negative strand, so long as it can be specifically detected in the assay being used. Similarly, an antibody may be labeled.

In certain multiplex formats, labels used for detecting different targets may be distinguishable. The label can be attached directly (e.g., via covalent linkage) or indirectly, e.g., via a bridging molecule or series of molecules (e.g., a molecule or complex that can bind to an assay component, or via members of a binding pair that can be incorporated into assay components, e.g. biotin-avidin or streptavidin). Many labels are commercially available in activated forms which can readily be used for such conjugation (for example through amine acylation), or labels may be attached through known or determinable conjugation schemes, many of which are known in the art.

Labels useful in the invention described herein include any substance which can be detected when bound to or incorporated into the biomolecule of interest. Any effective detection method can be used, including optical, spectroscopic, electrical, piezoelectrical, magnetic, Raman scattering, surface plasmon resonance, colorimetric, calorimetric, etc. A label is typically selected from a chromophore, a lumiphore, a fluorophore, one member of a quenching system, a chromogen, a hapten, an antigen, a magnetic particle, a material exhibiting nonlinear optics, a semiconductor nanocrystal, a metal nanoparticle, an enzyme, an antibody or binding portion or equivalent thereof, an aptamer, and one member of a binding pair, and combinations thereof. Quenching schemes may be used, wherein a quencher and a fluorophore as members of a quenching pair may be used on a probe, such that a change in optical parameters occurs upon binding to the target introduce or quench the signal from the fluorophore. One example of such a system is a molecular beacon. Suitable quencher/fluorophore systems are known in the art. The label may be bound through a variety of intermediate linkages. For example, a polynucleotide may comprise a biotin-binding species, and an optically detectable label may be conjugated to biotin and then bound to the labeled polynucleotide. Similarly, a polynucleotide sensor may comprise an immunological species such as an antibody or fragment, and a secondary antibody containing an optically detectable label may be added.

Chromophores useful in the methods described herein include any substance which can absorb energy and emit light. For multiplexed assays, a plurality of different signaling chromophores can be used with detectably different emission spectra. The chromophore can be a lumophore or a fluorophore. Typical fluorophores include fluorescent dyes, semiconductor nanocrystals, lanthanide chelates, polynucleotide-specific dyes and green fluorescent protein.

Coding schemes may optionally be used, comprising encoded particles and/or encoded tags associated with different polynucleotides of the invention. A variety of different coding schemes are known in the art, including fluorophores, including SCNCs, deposited metals, and RF tags.

Polynucleotides from the described target sequences may be employed as probes for detecting target sequences expression, for ligation amplification schemes, or may be used as primers for amplification schemes of all or a portion of a target sequences. When amplified, either strand produced by amplification may be provided in purified and/or isolated form.

In one embodiment, polynucleotides of the invention include a nucleic acid depicted in (a) any of SEQ ID NOs: 1-659; (b) an RNA form of any of the nucleic acids depicted in SEQ ID NOs: 1-659; (c) a peptide nucleic acid form of any of the nucleic acids depicted in SEQ ID NOs: 1-659; (d) a nucleic acid comprising at least 20 consecutive bases of any of (a-c); (e) a nucleic acid comprising at least 25 consecutive bases having at least 90% sequence identity to any of (a-c); and a complement to any of (a-e).

Complements may take any polymeric form capable of base pairing to the species recited in (a)-(e), including nucleic acid such as RNA or DNA, or may be a neutral polymer such as a peptide nucleic acid. Polynucleotides of the invention can be selected from the subsets of the recited nucleic acids described herein, as well as their complements.

In some embodiments, polynucleotides of the invention comprise at least 20 consecutive bases as depicted in SEQ ID NOs:1-659, or a complement thereto. The polynucleotides may comprise at least 21, 22, 23, 24, 25, 27, 30, 32, 35 or more consecutive bases as depicted in SEQ ID NOs: 1-659.

The polynucleotides may be provided in a variety of formats, including as solids, in solution, or in an array. The polynucleotides may optionally comprise one or more labels, which may be chemically and/or enzymatically incorporated into the polynucleotide.

In one embodiment, solutions comprising polynucleotide and a solvent are also provided. In some embodiments, the solvent may be water or may be predominantly aqueous. In some embodiments, the solution may comprise at least two, three, four, five, six, seven, eight, nine, ten, twelve, fifteen, seventeen, twenty or more different polynucleotides, including primers and primer pairs, of the invention. Additional substances may be included in the solution, alone or in combination, including one or more labels, additional solvents, buffers, biomolecules, polynucleotides, and one or more enzymes useful for performing methods described herein, including polymerases and ligases. The solution may further comprise a primer or primer pair capable of amplifying a polynucleotide of the invention present in the solution.

In some embodiments, one or more polynucleotides provided herein can be provided on a substrate. The substrate can comprise a wide range of material, either biological, nonbiological, organic, inorganic, or a combination of any of these. For example, the substrate may be a polymerized Langmuir Blodgett film, functionalized glass, Si, Ge, GaAs, GaP, SiO₂, SiN₄, modified silicon, or any one of a wide variety of gels or polymers such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, cross-linked polystyrene, polyacrylic, polylactic acid, polyglycolic acid, poly(lactide coglycolide), polyanhydrides, poly(methyl methacrylate), poly(ethylene-co-vinyl acetate), polysiloxanes, polymeric silica, latexes, dextran polymers, epoxies, polycarbonates, or combinations thereof. Conducting polymers and photoconductive materials can be used.

Substrates can be planar crystalline substrates such as silica based substrates (e.g. glass, quartz, or the like), or crystalline substrates used in, e.g., the semiconductor and microprocessor industries, such as silicon, gallium arsenide, indium doped GaN and the like, and includes semiconductor nanocrystals.

The substrate can take the form of an array, a photodiode, an optoelectronic sensor such as an optoelectronic semiconductor chip or optoelectronic thin-film semiconductor, or a biochip. The location(s) of probe(s) on the substrate can be addressable; this can be done in highly dense formats, and the location(s) can be microaddressable or nanoaddressable.

Silica aerogels can also be used as substrates, and can be prepared by methods known in the art. Aerogel substrates may be used as free standing substrates or as a surface coating for another substrate material.

The substrate can take any form and typically is a plate, slide, bead, pellet, disk, particle, microparticle, nanoparticle, strand, precipitate, optionally porous gel, sheets, tube, sphere, container, capillary, pad, slice, film, chip, multiwell plate or dish, optical fiber, etc. The substrate can be any form that is rigid or semi-rigid. The substrate may contain raised or depressed regions on which an assay component is located. The surface of the substrate can be etched using known techniques to provide for desired surface features, for example trenches, v-grooves, mesa structures, or the like.

Surfaces on the substrate can be composed of the same material as the substrate or can be made from a different material, and can be coupled to the substrate by chemical or physical means. Such coupled surfaces may be composed of any of a wide variety of materials, for example, polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, membranes, or any of the above-listed substrate materials. The surface can be optically transparent and can have surface Si—OH functionalities, such as those found on silica surfaces.

The substrate and/or its optional surface can be chosen to provide appropriate characteristics for the synthetic and/or detection methods used. The substrate and/or surface can be transparent to allow the exposure of the substrate by light applied from multiple directions. The substrate and/or surface may be provided with reflective “mirror” structures to increase the recovery of light.

The substrate and/or its surface is generally resistant to, or is treated to resist, the conditions to which it is to be exposed in use, and can be optionally treated to remove any resistant material after exposure to such conditions.

The substrate or a region thereof may be encoded so that the identity of the sensor located in the substrate or region being queried may be determined. Any suitable coding scheme can be used, for example optical codes, RFID tags, magnetic codes, physical codes, fluorescent codes, and combinations of codes.

Preparation of Probes and Primers

The polynucleotide probes or primers of the present invention can be prepared by conventional techniques well-known to those skilled in the art. For example, the polynucleotide probes can be prepared using solid-phase synthesis using commercially available equipment. As is well-known in the art, modified oligonucleotides can also be readily prepared by similar methods. The polynucleotide probes can also be synthesized directly on a solid support according to methods standard in the art. This method of synthesizing polynucleotides is particularly useful when the polynucleotide probes are part of a nucleic acid array.

Polynucleotide probes or primers can be fabricated on or attached to the substrate by any suitable method, for example the methods described in U.S. Pat. No. 5,143,854, PCT Publ. No. WO 92/10092, U.S. patent application Ser. No. 07/624,120, filed Dec. 6, 1990 (now abandoned), Fodor et al., Science, 251: 767-777 (1991), and PCT Publ. No. WO 90/15070). Techniques for the synthesis of these arrays using mechanical synthesis strategies are described in, e.g., PCT Publication No. WO 93/09668 and U.S. Pat. No. 5,384,261. Still further techniques include bead based techniques such as those described in PCT Appl. No. PCT/US93/04145 and pin based methods such as those described in U.S. Pat. No. 5,288,514. Additional flow channel or spotting methods applicable to attachment of sensor polynucleotides to a substrate are described in U.S. patent application Ser. No. 07/980,523, filed Nov. 20, 1992, and U.S. Pat. No. 5,384,261.

Alternatively, the polynucleotide probes of the present invention can be prepared by enzymatic digestion of the naturally occurring target gene, or mRNA or cDNA derived therefrom, by methods known in the art.

Thyroid Classification Methods

The present invention further provides methods for characterizing thyroid samples for the presence of malignant or benign thyroid nodule disease. The methods use the thyroid classification sets, probes and primers described herein to provide expression signatures or profiles from a test sample derived from a subject having or suspected of having thyroid cancer. In some embodiments, such methods involve contacting a test sample with thyroid classifying probes (either in solution or immobilized) under conditions that permit hybridization of the probe(s) to any target nucleic acid(s) present in the test sample and then detecting any probe:target duplexes formed as an indication of the presence of the target nucleic acid in the sample. Expression patterns thus determined are then compared to one or more reference profiles or signatures.

Optionally, the expression pattern can be normalized. The methods use the thyroid classification sets, probes and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to classify thyroid nodule tissue as malignant or benign.

In some embodiments, such methods involve the specific amplification of target sequences nucleic acid(s) present in the test sample using methods known in the art to generate an expression profile or signature which is then compared to a reference profile or signature.

In some embodiments, the invention further provides for diagnosing thyroid cancer, for prognosing patient outcome, and/or for designating treatment modalities.

In one embodiment, the methods generate expression profiles or signatures detailing the expression of the 659 target sequences having altered relative expression in malignant and benign thyroid disease disclosed herein. In one embodiment, the methods generate expression profiles or signatures detailing the expression of the subsets of these target sequences having 7 or 36 target sequences as described in the examples.

In one embodiment, the methods generate expression profiles or signatures detailing the expression of the target sequences as set forth in SEQ ID NOs: 1-7; the target sequences as set forth in SEQ ID NOs: 1-12 and 283-306; or the target sequences as set forth in SEQ ID NOs: 261, 657, 658, and 659.

In some embodiments, the methods detect increased relative expression of one or more target sequences in Group I corresponding to the expression products of SEQ IDs:1-4 and 8-282, and/or decreased relative expression of one or more target sequences in Group II corresponding to the expression products of SEQ ID NOs: 5-7, and 283-659, and thereby designate a sample as comprising malignant thyroid nodule disease. In some embodiments, increased relative expression of one or more target sequences in Group II and/or decreased relative expression of one or more target sequences in Group 1 and thereby designate a sample as comprising benign thyroid nodule disease.

In some embodiments, the methods detect combinations of expression levels of sequences exhibiting positive and negative correlation with a disease status. In one embodiment, the methods detect a minimal expression signature.

Any method of detecting and/or quantitating the expression of the encoded target sequences can in principle be used in the invention. Such methods can include Northern blotting, array or microarray hybridization, by enzymatic cleavage of specific structures (e.g., an Invader® assay, Third Wave Technologies, e.g. as described in U.S. Pat. Nos. 5,846,717, 6,090,543; 6,001,567; 5,985,557; and 5,994,069) and amplification methods, e.g. RT-PCR, including in a TaqMan® assay (PE Biosystems, Foster City, Calif., e.g. as described in U.S. Pat. Nos. 5,962,233 and 5,538,848), and may be quantitative or semi-quantitative, and may vary depending on the origin, amount and condition of the available biological sample. Combinations of these methods may also be used. For example, nucleic acids may be amplified, labeled and subjected to microarray analysis. Single-molecule sequencing (e.g., Illumina, Helicos, PacBio, ABI SOLID), in situ hybridization, bead-array technologies (e.g., Luminex xMAP, Illumina BeadChips), branched DNA technology (e.g., Panomics, Genisphere).

The expressed target sequences can be directly detected and/or quantitated, or may be copied and/or amplified to allow detection of amplified copies of the expressed target sequences or its complement. In some embodiments, degraded and/or fragmented RNA can be usefully analyzed for expression levels of target sequences, for example RNA having an RNA integrity number of less than 8.

In some embodiments, quantitative RT-PCR assays are used to measure the expression level of target sequences depicted in SEQ IDs: 1-659. In other embodiments, a GeneChip or microarray can be used to measure the expression of one or more of the target sequences.

Molecular assays measure the relative expression levels of the target sequences, which can be normalized to the expression levels of one or more control sequences, for example array control sequences and/or one or more housekeeping genes, for example GAPDH. Increased (or decreased) relative expression of the target sequences as described herein, including any of SEQ ID NOs: 1-659, may thus be used alone or in any combination with each other in the methods described herein. In addition, negative control probes may be included.

Diagnostic Samples

Diagnostic samples for use with the systems and in the methods of the present invention comprise nucleic acids suitable for providing RNAs expression information.

In principle, the biological sample from which the expressed RNA is obtained and analyzed for target sequence expression can be any material suspected of comprising thyroid cancer. The diagnostic sample can be a biological sample used directly in a method of the invention. Alternatively, the diagnostic sample can be a sample prepared from a biological sample.

In one embodiments, the sample or portion of the sample comprising or suspected of comprising thyroid cancer can be any source of biological material, including cells, tissue or fluid, including bodily fluids. Non-limiting examples of the source of the sample include an aspirate, a needle biopsy, a cytology pellet, a bulk tissue preparation or a section thereof obtained for example by surgery or autopsy, lymph fluid, blood, plasma, serum, tumors, and organs.

The samples may be archival samples, having a known and documented medical outcome, or may be samples from current patients whose ultimate medical outcome is not yet known. Samples to be analyzed for thyroid cancer are typically obtained as fine needle aspirates, a cytology smear, a liquid-based preparation (e.g., ThinPrep®), a cytology pellet, or as bulk samples obtained, for example, from a thyroidectomy. Where samples of a bodily fluid are obtained, cells or cell types may be isolated and/or purified therefrom. For example, circulating epithelial cells can be obtained from peripheral blood and analyzed as described herein. In some embodiments, magnetic separation can be used to obtain circulating epithelial cells (U.S. Pat. No. 6,136,182).

In some embodiments, the sample may be dissected prior to molecular analysis. The sample may be prepared via macrodissection of a bulk tumor specimen or portion thereof, or may be treated via microdissection, for example via Laser Capture Microdissection (LCM).

The sample may initially be provided in a variety of states, as fresh tissue, fresh frozen tissue, fine needle aspirates, and may be fixed or unfixed. Frequently, medical laboratories routinely prepare medical samples in a fixed state, which facilitates tissue storage. A variety of fixatives can be used to fix tissue to stabilize the morphology of cells, and may be used alone or in combination with other agents. Exemplary fixatives include crosslinking agents, alcohols, acetone, Bouin's solution, Zenker solution, Hely solution, osmic acid solution and Carnoy solution.

Crosslinking fixatives can comprise any agent suitable for forming two or more covalent bonds, for example an aldehyde. Sources of aldehydes typically used for fixation include formaldehyde, paraformaldehyde, glutaraldehyde or formalin. Preferably, the crosslinking agent comprises formaldehyde, which may be included in its native form or in the form of paraformaldehyde or formalin. One of skill in the art would appreciate that for samples in which crosslinking fixatives have been used special preparatory steps may be necessary including for example heating steps and proteinase-k digestion; see methods

One or more alcohols may be used to fix tissue, alone or in combination with other fixatives. Exemplary alcohols used for fixation include methanol, ethanol and isopropanol.

Formalin fixation is frequently used in medical laboratories. Formalin comprises both an alcohol, typically methanol, and formaldehyde, both of which can act to fix a biological sample.

Whether fixed or unfixed, the biological sample may optionally be embedded in an embedding medium. Exemplary embedding media used in histology including paraffin, Tissue-Tek® V.I.P.™, Paramat, Paramat Extra, Paraplast, Paraplast X-tra, Paraplast Plus, Peel Away Paraffin Embedding Wax, Polyester Wax, Carbowax Polyethylene Glycol, Polyfin™, Tissue Freezing Medium TFM™, Cryo-Gel™, and OCT Compound (Electron Microscopy Sciences, Hatfield, Pa.). Prior to molecular analysis, the embedding material may be removed via any suitable techniques, as known in the art. For example, where the sample is embedded in wax, the embedding material may be removed by extraction with organic solvent(s), for example xylenes. Kits are commercially available for removing embedding media from tissues. Samples or sections thereof may be subjected to further processing steps as needed, for example serial hydration or dehydration steps.

In some embodiments, the sample is a fixed, wax-embedded biological sample. Frequently, samples from medical laboratories are provided as fixed, wax-embedded samples, most commonly as formalin-fixed, paraffin embedded (FFPE) tissues.

Whatever the source of the biological sample, the target polynucleotide that is ultimately assayed can be prepared synthetically (in the case of control sequences), but typically is purified from the biological source and subjected to one or more preparative steps. The RNA may be purified to remove or diminish one or more undesired components from the biological sample or to concentrate it. Conversely, where the RNA is too concentrated for the particular assay, it may be diluted.

RNA Extraction

RNA can be extracted and purified from biological samples using any suitable technique. A number of techniques are known in the art, and several are commercially available (e.g., FormaPure™ nucleic acid extraction kit, Agencourt Biosciences, Beverly Mass., High Pure FFPE RNA Micro Kit™, Roche Applied Science, Indianapolis, Ind.). RNA can be extracted from frozen tissue sections using TRIzol (Invitrogen, Carlsbad, Calif.) and purified using RNeasy Protect kit (Qiagen, Valencia, Calif.). RNA can be further purified using DNAse I treatment (Ambion, Austin, Tex.) to eliminate any contaminating DNA. RNA concentrations can be made using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). RNA integrity can be evaluated by running electropherograms, and RNA integrity number (RIN, a correlative measure that indicates intactness of mRNA) can be determined using the RNA 6000 PicoAssay for the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).

Amplification and Hybridization

Following sample collection and nucleic acid extraction, the nucleic acid portion of the sample comprising RNA that is or can be used to prepare the target polynucleotide(s) of interest can be subjected to one or more preparative reactions. These preparative reactions can include in vitro transcription (IVT), labeling, fragmentation, amplification and other reactions. mRNA can first be treated with reverse transcriptase and a primer to create cDNA prior to detection, quantitation and/or amplification; this can be done in vitro with purified mRNA or in situ, e.g., in cells or tissues affixed to a slide.

By “amplification” is meant any process of producing at least one copy of a nucleic acid, in this case an expressed RNA, and in many cases produces multiple copies. An amplification product can be RNA or DNA, and may include a complementary strand to the expressed target sequence. DNA amplification products can be produced initially through reverse translation and then optionally from further amplification reactions. The amplification product may include all or a portion of a PSR, and may optionally be labeled. A variety of amplification methods are suitable for use, including polymerase-based methods and ligation-based methods. Exemplary amplification techniques include the polymerase chain reaction method (PCR), the ligase chain reaction (LCR), ribozyme-based methods, self sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), the use of Q Beta replicase, reverse transcription, nick translation, and the like.

Asymmetric amplification reactions may be used to preferentially amplify one strand representing the PSR that is used for detection as the target polynucleotide. In some cases, the presence and/or amount of the amplification product itself may be used to determine the expression level of a given PSR. In other instances, the amplification product may be used to hybridize to an array or other substrate comprising sensor polynucleotides which are used to detect and/or quantitate PSR expression.

The first cycle of amplification in polymerase-based methods typically forms a primer extension product complementary to the template strand. If the template is single-stranded RNA, a polymerase with reverse transcriptase activity is used in the first amplification to reverse transcribe the RNA to DNA, and additional amplification cycles can be performed to copy the primer extension products. The primers for a PCR must, of course, be designed to hybridize to regions in their corresponding template that will produce an amplifiable segment; thus, each primer must hybridize so that its 3′ nucleotide is paired to a nucleotide in its complementary template strand that is located 3′ from the 3′ nucleotide of the primer used to replicate that complementary template strand in the PCR.

The target polynucleotide can be amplified by contacting one or more strands of the target polynucleotide with a primer and a polymerase having suitable activity to extend the primer and copy the target polynucleotide to produce a full-length complementary polynucleotide or a smaller portion thereof. Any enzyme having a polymerase activity that can copy the target polynucleotide can be used, including DNA polymerases, RNA polymerases, reverse transcriptases, enzymes having more than one type of polymerase or enzyme activity. The enzyme can be thermolabile or thermostable. Mixtures of enzymes can also be used. Exemplary enzymes include: DNA polymerases such as DNA Polymerase I (“Pol I”), the Klenow fragment of Pol I, T4, T7, Sequenase® T7, Sequenase® Version 2.0 T7, Tub, Taq, Tth, Pfx, Pfu, Tsp, Tfl, Tli and Pyrococcus sp GB-D DNA polymerases; RNA polymerases such as E. coli, SP6, T3 and T7 RNA polymerases; and reverse transcriptases such as AMV, M-MuLV, MMLV, RNAse H⁻ MMLV (SuperScript®), SuperScript® II, ThermoScript®, HIV-1, and RAV2 reverse transcriptases. All of these enzymes are commercially available. Exemplary polymerases with multiple specificities include RAV2 and Tli (exo-) polymerases. Exemplary thermostable polymerases include Tub, Taq, Tth, Pfx, Pfu, Tsp, Tfl, Tli and Pyrococcus sp. GB-D DNA polymerases.

Suitable reaction conditions are chosen to permit amplification of the target polynucleotide, including pH, buffer, ionic strength, presence and concentration of one or more salts, presence and concentration of reactants and cofactors such as nucleotides and magnesium and/or other metal ions (e.g., manganese), optional cosolvents, temperature, thermal cycling profile for amplification schemes comprising a polymerase chain reaction, and may depend in part on the polymerase being used as well as the nature of the sample. Cosolvents include formamide (typically at from about 2 to about 10%), glycerol (typically at from about 5 to about 10%), and DMSO (typically at from about 0.9 to about 10%). Techniques may be used in the amplification scheme in order to minimize the production of false positives or artifacts produced during amplification. These include “touchdown” PCR, hot-start techniques, use of nested primers, or designing PCR primers so that they form stem-loop structures in the event of primer-dimer formation and thus are not amplified. Techniques to accelerate PCR can be used, for example centrifugal PCR, which allows for greater convection within the sample, and comprising infrared heating steps for rapid heating and cooling of the sample. One or more cycles of amplification can be performed. An excess of one primer can be used to produce an excess of one primer extension product during PCR; preferably, the primer extension product produced in excess is the amplification product to be detected. A plurality of different primers may be used to amplify different target polynucleotides or different regions of a particular target polynucleotide within the sample.

An amplification reaction can be performed under conditions which allow an optionally labeled sensor polynucleotide to hybridize to the amplification product during at least part of an amplification cycle. When the assay is performed in this manner, real-time detection of this hybridization event can take place by monitoring for light emission or fluorescence during amplification, as known in the art.

Where the amplification product is to be used for hybridization to an array or microarray, a number of suitable commercially available amplification products are available. These include amplification kits available from NuGEN, Inc. (San Carlos, Calif.), including the WT-Ovation™ System, WT-Ovation™ System v2, WT-Ovation™ Pico System, WT-Ovation™ FFPE Exon Module, WT-Ovation™ FFPE Exon Module RiboAmp and RiboAmp^(Plus) RNA Amplification Kits (MDS Analytical Technologies (formerly Arcturus) (Mountain View, Calif.), Genisphere, Inc. (Hatfield, Pa.), including the RampUp Plus™ and SenseAmp™ RNA Amplification kits, alone or in combination. Amplified nucleic acids may be subjected to one or more purification reactions after amplification and labeling, for example using magnetic beads (e.g., RNAClean magnetic beads, Agencourt Biosciences).

Multiple RNA biomarkers can be analyzed using real-time quantitative multiplex RT-PCR platforms and other multiplexing technologies such as GenomeLab GeXP Genetic Analysis System (Beckman Coulter, Foster City, Calif.), SmartCycler® 9600 or GeneXpert® Systems (Cepheid, Sunnyvale, Calif.), ABI 7900 HT Fast Real Time PCR system (Applied Biosystems, Foster City, Calif.), LightCycler® 480 System (Roche Molecular Systems, Pleasanton, Calif.), xMAP 100 System (Luminex, Austin, Tex.) Solexa Genome Analysis System (Illumina, Hayward, Calif.), OpenArray Real Time qPCR (BioTrove, Woburn, Mass.) and BeadXpress System (Illumina, Hayward, Calif.).

Thyroid Classification Arrays

The present invention contemplates that a thyroid classification set or probes derived therefrom may be provided in an array format. In the context of the present invention, an “array” is a spatially or logically organized collection of polynucleotide probes. Any array comprising sensor probes specific for two or more of the target sequences depicted in SEQ ID NOs: 1-659 or a product derived from the target sequences depicted therein can be used. Desirably, an array will be specific for 5, 10, 15, 20, 25, 30, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, or more of SEQ ID NOs: 1-659. Expression of these sequences may be detected alone or in combination with other transcripts. In some embodiments, an array is used which comprises a wide range of sensor probes for thyroid-specific expression products, along with appropriate control sequences. An array of interest is the Human Exon 1.0 ST Array (HuEx 1.0 ST, Affymetrix, Inc., Santa Clara, Calif.).

In one embodiment, the array comprising sensor probes specific for the target sequences as set forth in SEQ ID NOs: 1-7; the target sequences as set forth in SEQ ID NOs: 1-12 and 283-306; or the target sequences as set forth in SEQ ID NOs: 261, 657, 658, and 659.

Typically the polynucleotide probes are attached to a solid substrate and are ordered so that the location (on the substrate) and the identity of each are known. The polynucleotide probes can be attached to one of a variety of solid substrates capable of withstanding the reagents and conditions necessary for use of the array. Examples include, but are not limited to, polymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, polycarbonate, polypropylene and polystyrene; ceramic; silicon; silicon dioxide; modified silicon; (fused) silica, quartz or glass; functionalized glass; paper, such as filter paper; diazotized cellulose; nitrocellulose filter; nylon membrane; and polyacrylamide gel pad. Substrates that are transparent to light are useful for arrays that will be used in an assay that involves optical detection.

Examples of array formats include membrane or filter arrays (for example, nitrocellulose, nylon arrays), plate arrays (for example, multiwell, such as a 24-, 96-, 256-, 384-, 864- or 1536-well, microtitre plate arrays), pin arrays, and bead arrays (for example, in a liquid “slurry”). Arrays on substrates such as glass or ceramic slides are often referred to as chip arrays or “chips.” Such arrays are well known in the art. In one embodiment of the present invention, the thyroid classification array is a chip.

Data Analysis

Array data can be managed and analyzed using techniques known in the art. The Genetrix suite of tools can be used for microarray analysis (Epicenter Software, Pasadena, Calif.). Probe set modeling and data pre-processing can be derived using the Robust Multi-Array (RMA) algorithm or variant GC-RMA, Probe Logarithmic Intensity Error (PLIER) algorithm or variant iterPLIER. Variance or intensity filters can be applied to pre-process data using the RMA algorithm, for example by removing target sequences with a standard deviation of <10 or a mean intensity of <100 intensity units of a normalized data range, respectively.

In some embodiments, one or more pattern recognition methods can be used in analyzing the expression level of target sequences. The pattern recognition method can comprise a linear combination of expression levels, or a nonlinear combination of expression levels. In some embodiments, expression measurements for RNA transcripts or combinations of RNA transcript levels are formulated into linear or non-linear models or algorithms (i.e., an ‘expression signature’) and converted into a likelihood score. This likelihood score indicates the probability that a biological sample is from malignant thyroid nodule disease or benign disease. The likelihood score can be used to distinguish malignant from benign thyroid nodule disease. The models and/or algorithms can be provided in machine readable format, and may be used to correlate expression levels or an expression profile with a disease state, and/or to designate a treatment modality for a patient or class of patients.

Thus, results of the expression level analysis can be used to correlate increased expression of one or more target sequences in group I (or a subset thereof) and/or decreased expression of one or more target sequences in group II (or a subset thereof) with thyroid cancer, and to designate a treatment modality selected from total thyroidectomy, radioactive iodine treatment, and a combination thereof. Patients with benign disease would be candidates for watchful waiting (careful monitoring at regular intervals), thyroid hormone suppression therapy (treating with levothyroxine or other synthetic forms of thyroxine) to shrink the nodule, radioactive iodine to treat hyperfunctioning adenomas or multinodular goiters and surgery. For benign patients surgical management is much more limited to either cosmetic debulking procedures or only partial thyroidectomy leaving thyroid function largely intact. In contrast, the usual treatment for malignant nodules is surgical removal using more aggressive approaches such as near-total or total thyroidectomy followed by radioactive iodine ablation therapy and permanent thyroid hormone replacement therapy. Results of the expression level analysis can be used to correlate increased expression of one or more target sequences in group II (or a subset thereof) and/or decreased expression of one or more target sequences in group I (or a subset thereof) with benign disease, and to designate a treatment modality selected from near-total thyroidectomy, partial thyroidectomy, or watchful-waiting. The preferred treatment regimen for benign or non-neoplastic disease is observation.

Factors known in the art for diagnosing and/or suggesting, selecting, designating, recommending or otherwise determining a course of treatment for a patient or class of patients suspected of having thyroid disease can be employed in combination with measurements of the target sequence expression. These techniques include FNAB cytology and classification, ultrasound analysis, MRI results, CT scan results, thyroid scans, and measurements of thyroid hormone levels.

For example, factors which may be used to indicate a benign condition include a family history of Hashimoto's thyroiditis, of benign thyroid nodule, or of goiter, symptoms of hyper- or hypothyroidism, pain or tenderness associated with a nodule, a nodule that is soft, smooth and mobile, a multinodular goiter without a predominant nodule, a nodule that is “warm” on a thyroid scan, or an ultrasound indication of a simple cyst structure.

Factors which may be used to indicate a malignant thyroid condition include patient age less than 20 or greater than seventy, male gender, new onset of swallowing difficulties or hoarseness, a history of external neck irradiation, a nodule that is firm, irregular and fixed, cervical lymphadenopathy, a history of thyroid cancer, a nodule that is “cold” on a thyroid scan, and a solid or complex morphology seen on ultrasound.

Certified tests for classifying thyroid disease status and/or designating treatment modalities are also provided. A certified test comprises a means for characterizing the expression levels of one or more of the target sequences of interest, and a certification from a government regulatory agency endorsing use of the test for classifying the thyroid disease status of a biological sample.

In some embodiments, the certified test may comprise reagents for amplification reactions used to detect and/or quantitate expression of the target sequences to be characterized in the test. An array of probe nucleic acids can be used, with or without prior target amplification, for use in measuring target sequence expression.

The test is submitted to an agency having authority to certify the test for use in distinguishing benign from malignant thyroid tissues. Results of detection of expression levels of the target sequences used in the test and correlation with disease status and/or outcome are submitted to the agency. A certification authorizing the diagnostic and/or prognostic use of the test is obtained.

Also provided are portfolios of expression levels comprising a plurality of normalized expression levels of the target sequences described herein, including SEQ ID NOs: 1-659. Such portfolios may be provided by performing the methods described herein to obtain expression levels from an individual patient or from a group of patients. The expression levels can be normalized by any method known in the art; exemplary normalization methods that can be used in various embodiments include Robust Multichip Average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based and nonlinear normalization, and combinations thereof. Background correction can also be performed on the expression data; exemplary techniques useful for background correction include mode of intensities, normalized using median polish probe modeling and sketch-normalization.

In some embodiments, portfolios are established such that the combination of genes in the portfolio exhibit improved sensitivity and specificity relative to known methods. In considering a group of genes for inclusion in a portfolio, a small standard deviation in expression measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity. The invention also encompasses the above methods where the specificity is at least about 50% and at least about 60%. The invention also encompasses the above methods where the sensitivity is at least about 90%.

The gene expression profiles of each of the target sequences comprising the portfolio can fixed in a medium such as a computer readable medium. This can take a number of forms. For example, a table can be established into which the range of signals (e.g., intensity measurements) indicative of disease is input. Actual patient data can then be compared to the values in the table to determine whether the patient samples are normal, benign or diseased. In a more sophisticated embodiment, patterns of the expression signals (e.g., fluorescent intensity) are recorded digitally or graphically.

Comparisons can also be used to determine whether the patient is not likely to experience the disease. The expression profiles of the samples are then compared to a control portfolio. If the sample expression patterns are consistent with the expression pattern for cancer then (in the absence of countervailing medical considerations) the patient is treated as one would treat a thyroid cancer patient. If the sample expression patterns are consistent with the expression pattern from the normal/control cell then the patient is diagnosed negative for cancer.

Genes can be grouped so that information obtained about the set of genes in the group can be used to make or assist in making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice.

A patient report is also provided comprising a representation of measured expression levels of a plurality of target sequences in a biological sample from the patient, wherein the representation comprises expression levels of target sequences corresponding to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences depicted in SEQ ID NOs: 1-659, or of the subsets described herein, or of a combination thereof. In some embodiments, the representation of the measured expression level(s) may take the form of a linear or nonlinear combination of expression levels of the target sequences of interest. The patient report may be provided in a machine (e.g., a computer) readable format and/or in a hard (paper) copy. The report can also include standard measurements of expression levels of said plurality of target sequences from one or more sets of patients with known thyroid status and/or outcome. The report can be used to inform the patient and/or treating physician of the expression levels of the expressed target sequences, the likely medical diagnosis and/or implications, and optionally may recommend a treatment modality for the patient.

Also provided are representations of the gene expression profiles useful for treating, diagnosing, prognosticating, and otherwise assessing disease. In some embodiments, these profile representations are reduced to a medium that can be automatically read by a machine such as computer readable media (magnetic, optical, and the like). The articles can also include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a readable storage form having computer instructions for comparing gene expression profiles of the portfolios of genes described above. The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the profiles can be recorded in different representational format. A graphical recordation is one such format. Clustering algorithms can assist in the visualization of such data.

Kits

Kits for performing the desired method(s) are also provided, and comprise a container or housing for holding the components of the kit, one or more vessels containing one or more nucleic acid(s), and optionally one or more vessels containing one or more reagents. The reagents include those described in the composition of matter section above, and those reagents useful for performing the methods described, including amplification reagents, and may include one or more probes, primers or primer pairs, enzymes (including polymerases and ligases), intercalating dyes, labeled probes, and labels that can be incorporated into amplification products.

In some embodiments, the kit comprises primers or primer pairs specific for those subsets and combinations of target sequences described herein. At least two, three, four or five primers or pairs of primers suitable for selectively amplifying the same number of target sequence-specific polynucleotides can be provided in kit form. In some embodiments, the kit comprises from five to fifty primers or pairs of primers suitable for amplifying the same number of target sequence-representative polynucleotides of interest.

The reagents may independently be in liquid or solid form. The reagents may be provided in mixtures. Control samples and/or nucleic acids may optionally be provided in the kit. Control samples may include tissue and/or nucleic acids obtained from or representative of benign thyroid tissue, as well as tissue and/or nucleic acids obtained from or representative of malignant thyroid tissue.

The nucleic acids may be provided in an array format, and thus an array or microarray may be included in the kit. The kit optionally may be certified by a government agency for use in classifying the disease status of thyroid tissue and/or for designating a treatment modality.

Instructions for using the kit to perform one or more methods of the invention can be provided with the container, and can be provided in any fixed medium. The instructions may be located inside or outside the container or housing, and/or may be printed on the interior or exterior of any surface thereof. A kit may be in multiplex form for concurrently detecting and/or quantitating one or more different target polynucleotides representing the expressed target sequences.

Devices

Devices useful for performing methods of the invention are also provided. The devices can comprise means for characterizing the expression level of a target sequence of the invention, for example components for performing one or more methods of nucleic acid extraction, amplification, and/or detection. Such components may include one or more of an amplification chamber (for example a thermal cycler), a plate reader, a spectrophotometer, capillary electrophoresis apparatus, a chip reader, and or robotic sample handling components. These components ultimately can obtain data that reflects the expression level of the target sequences used in the assay being employed.

The devices may include an excitation and/or a detection means. Any instrument that provides a wavelength that can excite a species of interest and is shorter than the emission wavelength(s) to be detected can be used for excitation. Commercially available devices can provide suitable excitation wavelengths as well as suitable detection components.

Exemplary excitation sources include a broadband UV light source such as a deuterium lamp with an appropriate filter, the output of a white light source such as a xenon lamp or a deuterium lamp after passing through a monochromator to extract out the desired wavelength(s), a continuous wave (cw) gas laser, a solid state diode laser, or any of the pulsed lasers. Emitted light can be detected through any suitable device or technique; many suitable approaches are known in the art. For example, a fluorimeter or spectrophotometer may be used to detect whether the test sample emits light of a wavelength characteristic of a label used in an assay.

The devices typically comprise a means for identifying a given sample, and of linking the results obtained to that sample. Such means can include manual labels, barcodes, and other indicators which can be linked to a sample vessel, and/or may optionally be included in the sample itself, for example where an encoded particle is added to the sample. The results may be linked to the sample, for example in a computer memory that contains a sample designation and a record of expression levels obtained from the sample. Linkage of the results to the sample can also include a linkage to a particular sample receptacle in the device, which is also linked to the sample identity.

The devices also comprise a means for correlating the expression levels of the target sequences being studied with a classification of thyroid disease. Such means may comprise one or more of a variety of correlative techniques, including lookup tables, algorithms, multivariate models, and linear or nonlinear combinations of expression models or algorithms. The expression levels may be converted to one or more likelihood scores, reflecting the likelihood that the sample comprises malignant tissue and/or the likelihood that the sample comprises benign tissue. The models and/or algorithms can be provided in machine readable format, and can optionally further designate a treatment modality for a patient or class of patients The device also comprises output means for outputting the thyroid disease status and/or a treatment modality. Such output means can take any form which transmits the results to a patient and/or a healthcare provider, and may include a monitor, a printed format, or both. The device may use a computer system for performing one or more of the steps provided.

CITATIONS

-   1: Griffith O L, et al., “Meta-analysis and meta-review of thyroid     cancer gene expression profiling studies identifies important     diagnostic biomarkers,” J Clin Oncol. 2006 Nov. 1, 24(31):5043-51. -   2: Puskas L G, et al., “Gene profiling identifies genes specific for     well-differentiated epithelial thyroid tumors,” Cell Mol Biol     (Noisy-le-grand), 2005 Sep. 5, 51(2):177-86. -   3: Fujarewicz K, et al., “A multi-gene approach to differentiate     papillary thyroid carcinoma from benign lesions: gene selection     using support vector machines with bootstrapping,” Endocr Relat     Cancer. 2007 September, 14(3):809-26. -   4: Kebebew E, et al., “Diagnostic and extent of disease multigene     assay for malignant thyroid neoplasms,” Cancer. 2006 Jun. 15,     106(12):2592-7. -   5: Finley D J, et al., “Discrimination of benign and malignant     thyroid nodules by molecular profiling,” Ann Surg. 2004 September,     240(3):425-36; discussion 436-7. -   6: Mazzanti C, et al., “Using gene expression profiling to     differentiate benign versus malignant thyroid tumors,” Cancer Res.     2004 Apr. 15:64(8):2898-903. Erratum in: Cancer Res. 2004 Jul. 15,     64(14):5028. -   7: Finley D J, et al., “Advancing the molecular diagnosis of thyroid     nodules: defining benign lesions by molecular profiling,” Thyroid.     2005 June; 15(6):562-8. -   8: Cerutti J M, et al., “Diagnosis of suspicious thyroid nodules     using four protein biomarkers,” Clin Cancer Res. 2006 Jun. 1; 12(11     Pt 1):3311-8. -   9: Fryknäs M, et al., “Molecular markers for discrimination of     benign and malignant follicular thyroid tumors,” Tumour Biol. 2006;     27(4):211-20. -   10: Hamada A, et al., “Diagnostic usefulness of PCR profiling of the     differentially expressed marker genes in thyroid papillary     carcinomas,” Cancer Lett. 2005 Jun. 28, 224(2):289-301. -   11: Yukinawa N, et al., “A multi-class predictor based on a     probabilistic model: application to gene expression profiling-based     diagnosis of thyroid tumors,” BMC Genomics. 2006 Jul. 27, 7:190. -   12: Griffiths O L, et al., “Biomarker panel diagnosis of thyroid     cancer: a critical review,” Expert Rev. Anticancer Therapy. 2008     September, 8(9): 1399-1413. -   13. Prasad N B, et al., “Identification of Genes Differentially     Expressed in Benign versus Malignant Thyroid Tumors,” Clinical     Cancer Res. 2008 Jun. 1, 14(11):3327-37. -   14. Shibru D, et al., “Does the 3-gene diagnostic assay accurately     distinguish benign from malignant thyroid neoplasms?” Cancer. 2008     Sep. 1; 113(5):930-5. -   15. Fontaine J F, et al., “Increasing the number of thyroid lesions     classes in microarray analysis improves the relevance of diagnostic     markers,” PLoS One. 2009 Oct. 29; 4(10):e7632.

PATENT DOCUMENTS

-   1. US 2005/0042222 -   2. US 2007/0037186 -   3. U.S. Pat. No. 7,319,011 -   4. WO 2005/085471 -   5. WO 2005/085471 -   6. WO 2005/100608 -   7. WO 2005/100608 -   8. WO 2006/047484 -   9. WO 2006/127537

EXAMPLES

To gain a better understanding of the invention described herein, the following examples are set forth. It will be understood that these examples are intended to describe illustrative embodiments of the invention and are not intended to limit the scope of the invention in any way. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperature, etc.) but some experimental error and deviation should be accounted for. Unless otherwise indicated, parts are parts by weight, temperature is degree centigrade and pressure is at or near atmospheric, and all materials are commercially available.

Example 1 Identification of Target Sequences Differentially Expressed in Benign and Malignant Thyroid

One hundred and ten genes were identified as having increased expression in malignant thyroid nodule disease tissue relative to benign thyroid nodule disease tissue, and fifty eight genes were identified as having increased relative expression in benign thyroid disease relative to malignant thyroid nodule disease from a detailed literature review (see citations #1-15) (Table 1). The 110 and 58 gene lists were used to identify 5491 and 3651 respective target sequences (i.e., probesets) from these genomic regions on the Affymetrix Human Exon 1.0 ST GeneChip microarray, totaling 9,142 candidate target sequences.

These candidate 9,149 target sequences were further analyzed through several rounds of technical and biological data filtering to select those most likely to provide a selective, reproducible assay. The candidate target sequences were evaluated for potential cross-hybridization with transcribed sequences that would interfere with unique detection. Only those target sequences that were predicted to uniquely hybridize to a single target were considered further, reducing the candidate target sequences to 7,826 (i.e., removing 1,252 probeset)). Probesets with known cross-hybridization properties (i.e., probesets that map to multiple locations in the human genome) were obtained from the supporting annotation files downloaded from the Affymetrix website (www.affymetrix.com). Further biological filtering of probesets was performed using a microarray dataset of whole-transcriptome expression profiles from 48 thyroid nodule disease specimens (formalin-fixed paraffin embedded thyroidectomy samples with pathological characteristics as indicated in Table 2). Affymetrix HuEx 1.0 ST microarray expression data was modeled and normalized using the iterPLIER algorithm and log transformation. A background expression filter removed probesets whose maximal expression was lower than the background expression level (100 units of a normalized data range) in all 48 samples leaving 4,917 probesets for differential expression analysis. Differentially expressed probesets between malignant (n=23) and benign (n=25) (based on the ‘gold-standard’ pathology review diagnosis) thyroid nodule disease were determined based on three probeset selection criteria; a) at least 2-fold mean difference in expression, b) a student's t-test p<0.001 (with a Benjamini-Hochberg false discovery correction) and c) mean expression of >100 units (i.e., above background) in either the malignant or benign groups of thyroid nodule disease. With these probeset selection criteria, 279 (from 58 genes) and 377 (from 43 genes) probesets were found at increased expression in malignant and benign thyroid nodule disease samples, respectively (Table 3). Intriguingly, the majority of the probesets (59%) identified in this analysis do not overlap with the protein coding sequence (CDS) as indicated by the start point of initiation codon to the end point termination codon location of the translated protein from the mRNA (FIG. 1). Therefore, this data suggests that in the formalin-fixed paraffin embedded thyroidectomy specimens non-coding target sequences from thyroid nodule disease diagnostic genes identified in the literature are more likely to be detectable as differentially expressed between malignant and benign clinical samples than are the target sequences from protein coding sequence.

By applying the selection criteria from genes identified in literature as described above, we defined target sequences that are detectable and differentially expressed in routine thyroid nodule disease clinical samples (e.g., FFPE thyroidectomies) and therefore may be of diagnostic value for thyroid nodule disease. Increased relative expression of one or more encoded sequences from the 279 target sequences representing 58 genes in Table 3 having increased expression in thyroid cancer can be used to designate a sample as malignant thyroid cancer. Additionally, a decreased relative level of expression of one or more encoded sequences from the 377 target sequences representing the 43 genes in Table 3 can also be used to designate the sample as malignant thyroid cancer, alone or in combination with one or more increased expression levels from the 58 gene set.

Conversely, increased relative expression of one or more encoded sequences from the 377 target sequences representing the 43 genes in Table 3 having increased expression in benign disease can be used to designate a sample as benign. Similarly, a decreased relative level of expression of one or more encoded sequences from the 279 target sequences representing the 58 genes in Table 3 can also be used to designate the sample as benign, alone or in combination with one or more increased expression levels from the 43 gene set.

The data can be used to designate disease state, and/or to recommend or designate one or more treatment modalities for patients, to produce patient reports, and to prepare expression profiles. By measuring the expression level of each transcript in a patient sample and multiplying it by a weighting factor a linear combination of the expression signature (reduced to one variable) can be determined for each patient. Thresholds will be applied to determine whether or not the weighted expression signature score is indicative of malignant or benign thyroid nodule disease. Similarly, such expression data from the filtered subsets described below may also be employed in these techniques.

Example 2 Identification of Minimal Thyroid Nodule Disease Expression Signatures and Evaluation in Pre-Operative Patient Specimens

In order to determine the appropriate number of target sequences necessary to obtain a minimal expression signature we used the Nearest Shrunken Centroids (NSC) method. In this method (see US 20070031873), a standardized centroid is computed for each class. This is the average gene expression for each gene in each class divided by the within-class standard deviation for that gene. Nearest centroid classification was done under cross-validation, so that on each iteration, 5 out of 48 samples was removed for class centroid computation (˜10% cross-validation). In this manner, we identified a 36 target sequence signature (Table 4) with an estimated cross-validated error rate of 6.3%. We also identified a subset of 7 target sequences (Table 5), a minimal expression signature that maintained the overall cross-validated error rate of 6.3%. With both signatures, three samples were misclassified by the centroid classification approach of which all were follicular lesions: two follicular carcinomas had a predicted class of benign and one follicular adenoma had a predicted class of malignant.

Next, 36- and 7-target sequence ‘metagenes’ were created with the target sequences in Table 4 and 5 using a simple linear combination by measuring the expression levels of the corresponding RNAs for each patient and multiplying each measurement by a corresponding weighting factor and combining these values into a single variable. Weighting factors were derived from the test statistic coefficients from a t-test for significance when comparing malignant and benign pathology review diagnoses (Table 4). Patient outcome predictor (‘POP’) scores were generated from the metagene values for each patient by scaling and normalizing the metagene scores within a range of 0 to 100. We used arbitrary cut-points to establish a diagnostic criteria—a value of between 0-40 points was used to indicate a patient with benign thyroid nodule disease and 40-100 points a patient with malignant thyroid nodule disease (FIG. 2). Using these cut-point criteria, the evaluation of the 7-RNA target sequence metagene in the training subset revealed that 22/23 patients with malignant thyroid nodule disease had POP scores above 40 points and 24/25 patients with benign thyroid nodule disease had scores below 40 points (Table 6). Using the same cut-point criteria, the 36-RNA target sequence metagene in the training subset revealed that 20/23 patients with malignant thyroid nodule disease had POP scores above 40 points and 24/25 patients with benign thyroid nodule disease had scores below 40 points (Table 6). The 7- and 36-RNA target sequence metagenes were further validated on two independent subsets of ten and eight patient specimens (see Table 2) that were not used to identify or select the target sequences described in Table 3. The ten specimen ‘follicular lesion only’ subset were from patients diagnosed with follicular pattern lesions, that were all indeterminate or ‘suspicious for cancer’ based on cytology fine-needle aspirate diagnosis. All ten of these patients were treated with total-thyroidectomy however, pathology review diagnosis revealed that only five of these patients actually had a malignant thyroid nodule disease lesion warranting total thyroidectomy. For the 7-RNA target sequence metagene, all five of the follicular adenoma (i.e., patients diagnosed with benign disease) specimens had POP scores of less than 40 points and 4/5 patients with follicular carcinoma or follicular variant of papillary carcinoma had scores of greater than 40 points (Table 6). For the 7-RNA target sequence metagene, all five of the follicular adenoma (i.e., patients diagnosed with benign disease) specimens had POP scores of less than 40 points and 4/5 patients with follicular carcinoma or follicular variant of papillary carcinoma had scores of greater than 40 points (Table 6). For the 36-RNA target sequence metagene, all five of the follicular adenoma (i.e., patients diagnosed with benign disease) specimens had POP scores of less than 40 points and 3/5 patients with follicular carcinoma or follicular variant of papillary carcinoma had scores of greater than 40 points (Table 6). In the second validation subset of specimens, eight samples were from cell blocks prepared from pre-operative fine-needle aspirates. These samples were from a subset of eight patients whose post-operative FFPE thyroidectomy specimens were profiled in the training subset. POP scores for these specimens showed that 3/3 patients with pathology review benign diagnoses had scores of less than 40 points and 5/5 patients with pathology review malignant diagnoses had scores of greater than 40 points. POP scores derived from pre- (i.e., FNA cell blocks) and post-operative (i.e., TMA FFPE thyroidectomy) specimens from the 7- and 36-RNA target sequence metagenes were highly correlated, both with an R² value of 0.88 (FIG. 3). This data suggests that the 7- and 36-RNA target sequence metagene expression signatures and derived POP scores can be applied to exact an identical diagnosis from patients either before the surgery via a fine-needle aspiration of thyroid cells or after the thyroid is removed from the patient. In order to evaluate the diagnostic accuracy of the 7- and 36-RNA metagene POP scores in comparison to the ‘gold-standard’ review pathology diagnosis in a manner where no a priori cut-points are pre-determined, receiver-operator characteristic (ROC) curves were generated. ROC curves for the 7- and 36-RNA metagene POP scores are depicted in FIGS. 4 and 5 and show that the area-under-the curve was above 96% and 97%, respectively demonstrating that both expression signatures and the POP specimen scoring method are performing extremely well as a diagnostic test.

Example 3 Differential Expression of SPOCK1 Target Sequences

Intriguingly, several of target sequences showed the reverse correlation in terms of differential expression than a gene-level analysis would suggest. For example, the gene SPOCK1 has been previously reported as found at increased expression in malignant than benign thyroid nodule disease (see Prasad N B et al., citation #13).

However, on the individual RNA transcript level over 2- and 1.8-fold mean increased expression of exon 11 and intron 2 RNAs, respectively was observed in malignant (rel. to benign) thyroid nodule disease samples, whereas nearly 8.6- and 1.2-fold mean increased expression of intron 6 and intron 3 RNAs, respectively was observed in benign (rel. to malignant) thyroid nodule disease samples (Table 7). Furthermore, the protein-coding strand of the SPOCK1 by convention occurs of the negative (or alternatively, ‘bottom’) strand and while the RNAs representing the target sequence for the protein-encoding exon 11 RNA is transcribed in this sense direction, the target sequences for introns 2, 3 and 6 are transcribed in an antisense direction to transcription, that is off the positive (or alternatively, ‘top’) strand of the gene. Using these four SPOCK1 RNAs, a SPOCK1-target sequence metagene was generated as described in Example #1. FIG. 6 shows the mean metagene values for of these SPOCK1 RNAs in the three subsets of patient specimens analyzed. The differences in mean overall expression for SPOCK1 RNAs clearly distinguish malignant from benign thyroid nodule disease in the training subset and the FNAB subset. In the follicular lesion subset, the mean metagene values did show some overlap. In summary, this data suggests that gene-level analysis (e.g., using 3′ biased microarrays such as U133 Plus 2.0) can miss important differences in transcription such as intron retention, antisense transcription, alternative splicing or exon usage and non-coding (i.e., translated into protein) RNA expression observed in this whole-transcriptome analysis. In particular, non-coding RNA the predominant RNA species (over 90% of the transcription in the genome) represent functional RNA molecules that are thought to convey key differences between pathological conditions through regulatory roles of protein-encoding gene expression. This is a potentially rich source of diagnostic information that cannot be captured by solely observing differences in protein-encoding gene expression or protein biomarker expression and may improve the diagnosis of specific pathological conditions of clinical importance such as malignant vs. benign in thyroid nodule disease.

These scoring systems can be used to select or guide the selection of treatment modalities such as observation and/or thyroid lobectomy for benign thyroid nodule disease patients, and total thyroid resection for malignant thyroid nodule disease. Other metagenes can similarly be created using other combinations of target sequence expression, for example using one or more of the target sequences depicted in any of the Tables.

Materials and Methods:

Tissue Samples.

Sixty (60) formalin-fixed paraffin embedded (FFPE) surgical specimens of human thyroid nodule disease were collected from patients at the Department of Surgery, St. Paul's Hospital (Vancouver, BC, Canada) according to an institutional review board-approved protocol. For a subset of 8 surgical specimens, fine-needle aspirate cell blocks were also available. For surgical specimens, a tissue microarrayer (Beecher Instruments, Silver Spring, Md.) was used to core each FFPE surgical resected specimen once with either a 0.6 mm or 1.0 diameter cylinder (‘FFPE TMA’). Surgical resected samples from 60 patients were evaluated. These samples were divided into three subsets consisting of a training (n=48) and a validation (n=10) subset of patients. Table 1 shows the composition of the subsets including the original cytology diagnosis as well as the ‘gold-standard’ pathology review diagnosis (obtained from careful dissection and histopathological analysis of specimens after thyroidectomy procedures). From cell blocks, 3 ten micron sections were obtained using a microtome. Cell block specimens were matched to 8 of the FFPE TMA surgical specimen samples evaluated.

Extraction of RNA.

RNA was extracted and purified from the FFPE TMA cores using a modified protocol for the commercially available Formapure nucleic acid extraction kit (Agencourt Biosciences, Beverly Mass.). Principal modifications to the kit protocol included preheating the lysis buffer to 70° C. before immersing the FFPE sections in lysis buffer and then subjecting FFPE lysates to incubation at 99° C. for 1 min. In addition, FFPE samples were incubated with Proteinase K (20 ul of 40 mg/mL) for 16 hrs in a water bath at 55° C. RNA was further purified using DNAse I treatment (Ambion, Austin, Tex.) to eliminate any contaminating DNA. RNA was eluted with 300 ul of RNAse-free water and subsequently concentrated and purified using sodium acetate precipitation and a series of ethanol washes and resuspended in 15 ul of water. RNA concentrations were calculated using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). These additional purification steps significantly improved the yield of amplified material in subsequent steps described below (data not shown). RNA integrity was evaluated by running electropherograms and RNA integrity number, RIN (a correlative measure that indicates intactness of mRNA) was determined using the RNA 6000 PicoAssay for the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.). Sufficient RNA (75 ng) was obtained using these procedures from all 60 FFPE TMA cores and 8 cell block microtome sections.

Nucleic Acid Amplification and GencChip Hybridization.

Purified RNA was subjected to whole-transcriptome amplification using the WT-Ovation FFPE system including the WT-Ovation Exon and FL-Ovation Biotin V2 labeling modules, with the following modifications. Seventy-five (75) nanograms of RNA extracted from FFPE TMA cores or FNAB cell blocks was used to generate amplified Ribo-SPIA product. For the WT-Ovation Exon sense-target strand conversion kit 4 ug of Ribo-SPIA product were used. Between 2.5 and 5 micrograms of WT-Ovation Exon product were used to fragment and label using the FL-Ovation Biotin V2 labeling module and labeled product was hybridized to Affymetrix Human Exon 1.0 ST GeneChips following manufacturer's recommendations (Affymetrix, Santa Clara, Calif.).

Microarray Analysis.

All data management and analysis was conducted using the Genetrix suite of tools for microarray analysis (Epicenter Software, Pasadena, Calif.). Probe set modeling and data pre-processing were derived using the iterPlier algorithm (Affymetrix, Santa Clarita, Calif.). The mode of intensity values was used for background correction and sketch was used for normalization and probe modeling used a median polish routine. Outlier samples were identified by evaluating the median absolute deviation of the normalized expression values in each sample and 25^(th) percentile outlier samples. Two samples from the testing cohort were removed from further analysis because they were clearly outliers using both quality control metrics described above. A variance filter was applied to data pre-processed using the iterPlier algorithm, by removing probe set regions (PSRs) with a mean intensity of <10 intensity units of a normalized data range. PSRs are comprised of an average of four individual probes that interrogate the expression of RNA transcripts or portions thereof. PSR annotations and the sequences (RNAs) that they interrogate were downloaded from the Affymetrix website (www.netaffx.com). An additional filter employed was to remove PSRs with known cross-hybridization properties (i.e., significant homology to more than one transcript from different genes or loci), leaving 1,134,588 PSRs for further analysis. Cross-hybridization properties of PSRs were downloaded from the Affymetrix website. Supervised analysis of differentially expressed RNA transcripts was determined based on the fold difference in the average expression (at least 2 fold difference) and the associated t-test, with a p-value cut-off of p<0.001 between malignant and benign thyroid nodule disease samples using the final pathology review diagnosis obtained from analysis of surgical specimens as the benchmark diagnosis.

Although the invention has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the spirit and scope of the invention. All such modifications as would be apparent to one skilled in the art are intended to be included within the scope of the claims.

TABLE 1 Examples of Suitable Genes for Inclusion in a Thyroid Classification Library Gene Symbol Gene Thyroid Malignant- Increased Expression ADORA1 Adenosine A1 receptor ALOX5 Arachidonate 5-lipoxygenase AMIGO2 Adhesion molecule with Ig-like domain 2 APOE Apolipoprotein E ARHGAP11A Rho GTPase activating protein 11A ARMCX3 Armadillo repeat containing, X-linked 3 C7orf24 Chromosome 7 open reading frame 24 CA4 Carbonic anhydrase IV CAMK2N1 Calcium/calmodulin-dependent protein kinase II inhibitor 1 CCND1 Cyclin D1 CD44 CD44 molecule (Indian blood group) CD55 CD55 molecule, decay accelerating factor for complement (Cromer blood group) CDH16 Cadherin 16, KSP-cadherin CDH3 Cadherin 3, type 1, P-cadherin (placental) CDKN2A Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) CHI3L1 Chitinase 3-like 1 (cartilage glycoprotein-39) CHRDL1 Chordin-like 1 CHST2 Carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 CITED1 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 1 CKS2 CDC28 protein kinase regulatory subunit 2 CSNK1G2 Casein kinase 1, gamma 2 CST6 Cystatin E/M CTSC Cathepsin C CTSH Cathepsin H CTSS Cathepsin S DLG7 Discs, large homolog 7 (Drosophila) DPP4 Dipeptidyl-peptidase 4 (CD26, adenosine deaminase complexing protein 2) DUSP5 Dual specificity phosphatase 5 DUSP6 Dual specificity phosphatase 6 ENDOD1 Endonuclease domain containing 1 EPS8 Epidermal growth factor receptor pathway substrate 8 ETHE1 Ethylmalonic encephalopathy 1 ETV5 Ets variant gene 5 (ets-related molecule) FAM129A Family with sequence similarity 129, member A FAM129B Family with sequence similarity 129, member B FAM129C Family with sequence similarity 129, member C FBLN1 Fibulin 1 FN1 Fibronectin 1 GABBR2 Gamma-aminobutyric acid (GABA) B receptor, 2 GALE UDP-galactose-4-epimerase GALNT7 UDP-N-acetyl-alpha-D-galactosamine: polypeptide N-acetylgalactosaminyltransferase 7 (GalNAc- T7) GDF15 Growth differentiation factor 15 GJB3 Gap junction protein, beta 3, 31 kDa GPM6A Glycoprotein M6A HBD Hemoglobin, delta HLA-DMB Major histocompatibility complex, class II, DM beta HLA-DQA1 Major histocompatibility complex, class II, DQ alpha 1 HLA-DRA Major histocompatibility complex, class II, DR alpha HMGA2 High mobility group AT-hook 2 ICAM1 Intercellular adhesion molecule 1 (CD54), human rhinovirus receptor ICAM4 Intercellular adhesion molecule 4 (Landsteiner-Wiener blood group) IGFBP6 Insulin-like growth factor binding protein 6 IGSF1 Immunoglobulin superfamily, member 1 KCNJ2 Potassium inwardly-rectifying channel, subfamily J, member 2 KIAA0746 KIAA0746 protein KLK10 Kallikrein-related peptidase 10 KRT15 Keratin 15 KRT19 Keratin 19 LAMB3 Laminin, beta 3 LCN2 Lipocalin 2 LGALS3 Lectin, galactoside-binding, soluble, 3 LPL Lipoprotein lipase LRP4 Low density lipoprotein receptor-related protein 4 MET Met proto-oncogene (hepatocyte growth factor receptor) MPZL2 Myelin protein zero-like 2 MPZL3 Myelin protein zero-like 3 MRC2 Mannose receptor, C type 2 MUC1 Mucin 1, cell surface associated MYBPH Myosin binding protein H NELL2 NEL-like 2 (chicken) NMU Neuromedin U NRCAM Neuronal cell adhesion molecule NRIP1 Nuclear receptor interacting protein 1 P4HA2 Procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase), alpha polypeptide II PDLIM4 PDZ and LIM domain 4 PDZK1IP1 PDZK1 interacting protein 1 PLAU Plasminogen activator, urokinase PLXNC1 Plexin C1 PRMT8 Protein arginine methyltransferase 8 PROS1 Protein S (alpha) PRSS23 Protease, serine, 23 PSD3 Pleckstrin and Sec7 domain containing 3 PTPRE Protein tyrosine phosphatase, receptor type, E QPCT Glutaminyl-peptide cyclotransferase (glutaminyl cyclase) RCN3 Reticulocalbin 3, EF-hand calcium binding domain RET Ret proto-oncogene RXRG Retinoid X receptor, gamma S100A10 S100 calcium binding protein A10 SCG5 Secretogranin V (7B2 protein) SDC4 Syndecan 4 SERPINA1 Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1 SFTPB Surfactant, pulmonary-associated protein B SLC27A6 Solute carrier family 27 (fatty acid transporter), member 6 SLC34A2 Solute carrier family 34 (sodium phosphate), member 2 SLC5A5 Solute carrier family 5 (sodium iodide symporter), member 5 SLPI Secretory leukocyte peptidase inhibitor SOX4 SRY (sex determining region Y)-box 4 SPOCK1 Sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1 SPP1 Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1) ST14 Suppression of tumorigenicity 14 (colon carcinoma) SYN1 Synapsin I TACSTD2 Tumor-associated calcium signal transducer 2 TGFA Transforming growth factor, alpha TIAM1 T-cell lymphoma invasion and metastasis 1 TIMP1 TIMP metallopeptidase inhibitor 1 TM7SF4 Transmembrane 7 superfamily member 4 TNC Tenascin C (hexabrachion) TPD52L1 Tumor protein D52-like 1 TRIM14 Tripartite motif-containing 14 TUSC3 Tumor suppressor candidate 3 Thyroid Benign- Increased Expression ABCA8 ATP-binding cassette, sub-family A (ABC1), member 8 ANK2 Ankyrin 2, neuronal ARG2 Arginase, type II BCL2 B-cell CLL/lymphoma 2 BEX1 Brain expressed, X-linked 1 BMP8A Bone morphogenetic protein 8a C1orf212 Chromosome 1 open reading frame 212 CCL14 Chemokine (C-C motif) ligand 14 CCL21 Chemokine (C-C motif) ligand 21 CFD Complement factor D (adipsin) ChGn Chondroitin beta1,4 N-acetylgalactosaminyltransferase COL9A3 Collagen, type IX, alpha 3 CRABP1 Cellular retinoic acid binding protein 1 DDIT3 DNA-damage-inducible transcript 3 DIO1 Deiodinase, iodothyronine, type I DIO2 Deiodinase, iodothyronine, type II EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 ELMO1 Engulfment and cell motility 1 ENPP1 Ectonucleotide pyrophosphatase/phosphodiesterase 1 FABP4 Fatty acid binding protein 4, adipocyte FCGBP Fc fragment of IgG binding protein FHL1 Four and a half LIM domains 1 FZD4 Frizzled homolog 4 (Drosophila) HBA2 Hemoglobin, alpha 2 HLF Hepatic leukemia factor ID4 Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein ITM2A Integral membrane protein 2A ITPR1 Inositol 1,4,5-triphosphate receptor, type 1 KCNAB1 Potassium voltage-gated channel, shaker-related subfamily, beta member 1 KIT V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog LIPG Lipase, endothelial LRP2 Low density lipoprotein-related protein 2 MATN2 Matrilin 2 MPPED2 Metallophosphoesterase domain containing 2 MT1F Metallothionein 1F MT1G Metallothionein 1G MT1H Metallothionein 1H MT1M Metallothionein 1M MT1X Metallothionein 1X MT2A Metallothionein 2A MTF1 Metal-regulatory transcription factor 1 OPRM1 Opioid receptor, mu 1 PIP3-E Phosphoinositide-binding protein PIP3-E PPARGC1A Peroxisome proliferator-activated receptor gamma, coactivator 1 alpha PRIM2 Primase, DNA, polypeptide 2 (58 kDa) RAB23 RAB23, member RAS oncogene family RGS16 Regulator of G-protein signaling 16 RHOBTB3 Rho-related BTB domain containing 3 SLC1A1 Solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, system Xag), member 1 SLC26A4 Solute carrier family 26, member 4 SOD3 Superoxide dismutase 3, extracellular STT3A STT3, subunit of the oligosaccharyltransferase complex, homolog A (S. cerevisiae) STT3B STT3, subunit of the oligosaccharyltransferase complex, homolog B (S. cerevisiae) TCEAL2 Transcription elongation factor A (SII)-like 2 TFF3 Trefoil factor 3 (intestinal) TPO Thyroid peroxidase VEGFA Vascular endothelial growth factor A ZMAT4 Zinc finger, matrin type 4

TABLE 2 Pathological Characteristics of Thyroid Nodule Disease Specimens Used for Data Filtering and Validation of Candidate Target Sequences Training Subset N Fine-Needle Aspirate Cytology Goiter 14 Diagnosis Suspicious for Cancer 20 Cancer 14 Pathology Diagnosis Benign 25 Malignant 23 Review Histology Diagnosis Goiter 15 Thyroiditis 1 Hashimoto's Disease 1 Hurthle Cell Adenoma 4 Follicular Adenoma 4 Follicular Carcinoma 3 Papillary Carcinoma 20 n Follicular Lesion Subset Fine-Needle Aspirate Cytology Suspicious for Cancer 10 Diagnosis Pathology Diagnosis Benign 5 Malignant 5 Review Histology Diagnosis Follicular Adenoma 5 Follicular Carcinoma 3 Papillary Carcinoma 2 Pre-operative Cell Block Subset Fine-Needle Aspirate Cytology Goiter 2 Diagnosis Suspicious for Cancer 1 Cancer 5 Pathology Diagnosis Benign 3 Malignant 5 Review Histology Diagnosis Goiter 2 Hurthle Cell Adenoma 1 Papillary Carcinoma 5

TABLE 3 Thyroid Nodule Disease Candidate Gene RNA Sequences Probeset Target SEQ Overlaps Sequence ID ID MFD Weights Gene CDS Subtype 1 3420365 14 8.75 High mobility group AT-hook 2 NO intronic 2 3696401 11 8.59 Cadherin 3, type 1, P-cadherin NO exonic (placental) 3 3577628 3 8.12 Serpin peptidase inhibitor, clade A YES exonic (alpha-1 antiproteinase, antitrypsin), member 1 4 3415760 4 5.94 Insulin-like growth factor binding NO exonic protein 6 5 2537615 −14 −11.03 Thyroid peroxidase NO antisense 6 2727648 −5 −9.09 V-kit Hardy-Zuckerman 4 feline NO exonic sarcoma viral oncogene homolog 7 3367706 −31 −8.23 Metallophosphoesterase domain NO intronic containing 2 8 2828473 8 10.26 PDZ and LIM domain 4 NO exonic 9 2414960 24 8.9 Tumor-associated calcium signal NO exonic transducer 2 10 2526829 6 8.69 Fibronectin 1 NO antisense 11 2526817 13 8.66 Fibronectin 1 NO antisense 12 2598305 17 8.61 Fibronectin 1 NO intronic 13 2338243 28 8.2 Tumor-associated calcium signal NO exonic transducer 2 14 2598348 35 8.16 Fibronectin 1 NO intronic 15 2598349 21 8.15 Fibronectin 1 NO intronic 16 2598307 10 8.11 Fibronectin 1 YES exonic 17 2562439 24 7.98 Surfactant, pulmonary-associated NO exonic protein B 18 3577632 17 7.92 Serpin peptidase inhibitor, clade A YES exonic (alpha-1 antiproteinase, antitrypsin), member 1 19 2401582 4 7.82 UDP-galactose-4-epimerase NO exonic 20 2598368 32 7.78 Fibronectin 1 NO intronic 21 2598282 8 7.77 Fibronectin 1 NO intronic 22 3549675 12 7.77 Serpin peptidase inhibitor, clade A NO antisense (alpha-1 antiproteinase, antitrypsin), member 1 23 2526824 16 7.7 Fibronectin 1 NO antisense 24 2598340 15 7.67 Fibronectin 1 YES exonic 25 2598328 13 7.65 Fibronectin 1 YES exonic 26 3460512 4 7.64 High mobility group AT-hook 2 NO antisense 27 2598278 10 7.63 Fibronectin 1 NO intronic 28 3976357 2 7.58 Synapsin I YES antisense 29 3335899 8 7.56 Cystatin E/M NO exonic 30 2598367 10 7.55 Fibronectin 1 YES exonic 31 2598338 8 7.54 Fibronectin 1 YES exonic 32 2598326 10 7.51 Fibronectin 1 NO intronic 33 2598299 8 7.49 Fibronectin 1 YES exonic 34 3549673 9 7.45 Serpin peptidase inhibitor, clade A NO antisense (alpha-1 antiproteinase, antitrypsin), member 1 35 2598346 7 7.35 Fibronectin 1 YES exonic 36 2598372 9 7.31 Fibronectin 1 YES exonic 37 2598281 10 7.3 Fibronectin 1 YES exonic 38 2598374 11 7.27 Fibronectin 1 YES exonic 39 2598329 9 7.25 Fibronectin 1 YES exonic 40 2598352 10 7.2 Fibronectin 1 YES exonic 41 2400179 5 7.19 Calcium/calmodulin-dependent YES exonic protein kinase II inhibitor 1 42 2324040 5 7.19 Calcium/calmodulin-dependent NO exonic protein kinase II inhibitor 1 43 2375629 51 7.19 Chitinase 3-like 1 (cartilage NO exonic glycoprotein-39) 44 2598342 10 7.16 Fibronectin 1 YES exonic 45 3577630 12 7.15 Serpin peptidase inhibitor, clade A YES exonic (alpha-1 antiproteinase, antitrypsin), member 1 46 2526820 9 7.13 Fibronectin 1 NO antisense 47 2598358 17 7.12 Fibronectin 1 YES exonic 48 2598321 16 7.1 Fibronectin 1 YES intronic 49 2598302 10 7.09 Fibronectin 1 YES exonic 50 3536718 13 7.09 Lectin, galactoside-binding, soluble, 3 NO intronic 51 2598289 19 7.09 Fibronectin 1 YES exonic 52 3976358 7 7.07 Synapsin I YES antisense 53 3335898 7 7.07 Cystatin E/M YES exonic 54 2598296 14 7.06 Fibronectin 1 YES exonic 55 2598271 8 7.05 Fibronectin 1 YES exonic 56 2526809 10 7.05 Fibronectin 1 NO exonic 57 2598277 10 7.04 Fibronectin 1 YES exonic 58 2598280 8 7.01 Fibronectin 1 YES exonic 59 2562441 32 7.01 Surfactant, pulmonary-associated NO intronic protein B 60 2598320 10 7 Fibronectin 1 NO intronic 61 3460465 18 7 High mobility group AT-hook 2 NO antisense 62 2598313 8 6.98 Fibronectin 1 YES exonic 63 2598354 10 6.97 Fibronectin 1 YES exonic 64 2598286 10 6.96 Fibronectin 1 YES exonic 65 2526813 6 6.94 Fibronectin 1 NO antisense 66 2598332 8 6.93 Fibronectin 1 NO intronic 67 2598356 11 6.93 Fibronectin 1 YES exonic 68 2598267 18 6.9 Fibronectin 1 NO exonic 69 2598365 15 6.88 Fibronectin 1 NO intronic 70 3270357 6 6.86 Protein tyrosine phosphatase, NO exonic receptor type, E 71 2598301 9 6.85 Fibronectin 1 YES exonic 72 2598371 8 6.84 Fibronectin 1 YES exonic 73 2685324 5 6.83 Protein S (alpha) YES exonic 74 3536724 8 6.83 Lectin, galactoside-binding, soluble, 3 NO extra- genic 75 3451841 9 6.82 NEL-like 2 (chicken) YES exonic 76 2598270 11 6.82 Fibronectin 1 YES exonic 77 2598288 8 6.81 Fibronectin 1 YES exonic 78 2827683 41 6.8 Solute carrier family 27 (fatty acid YES exonic transporter), member 6 79 2598324 7 6.79 Fibronectin 1 YES exonic 80 2598303 9 6.79 Fibronectin 1 NO intronic 81 2562442 21 6.73 Surfactant, pulmonary-associated NO intronic protein B 82 2598363 10 6.71 Fibronectin 1 YES exonic 83 2598273 9 6.7 Fibronectin 1 YES exonic 84 2598304 2 6.66 Fibronectin 1 YES exonic 85 3020344 2 6.64 Met proto-oncogene (hepatocyte NO exonic growth factor receptor) 86 2325319 3 6.64 UDP-galactose-4-epimerase NO exonic 87 3460510 2 6.63 High mobility group AT-hook 2 NO antisense 88 2598339 8 6.61 Fibronectin 1 YES exonic 89 3371816 3 6.59 Low density lipoprotein receptor- NO extra- related protein 4 genic 90 2400181 4 6.59 Calcium/calmodulin-dependent YES exonic protein kinase II inhibitor 1 91 3536743 4 6.58 Lectin, galactoside-binding, soluble, 3 YES exonic 92 3536736 9 6.54 Lectin, galactoside-binding, soluble, 3 NO intronic 93 3634838 2 6.51 Cathepsin H YES exonic 94 3976359 3 6.46 Synapsin I NO antisense 95 2598268 10 6.46 Fibronectin 1 NO exonic 96 3451865 3 6.42 NEL-like 2 (chicken) NO intronic 97 2526825 8 6.41 Fibronectin 1 NO antisense 98 2721993 10 6.4 Solute carrier family 34 (sodium YES exonic phosphate), member 2 99 2827693 32 6.39 Solute carrier family 27 (fatty acid NO exonic transporter), member 6 100 2598309 12 6.38 Fibronectin 1 NO intronic 101 2722006 11 6.37 Solute carrier family 34 (sodium NO exonic phosphate), member 2 102 2598330 19 6.37 Fibronectin 1 YES exonic 103 2598283 4 6.35 Fibronectin 1 NO intronic 104 2685316 3 6.34 Protein S (alpha) YES exonic 105 3666402 8 6.34 Cadherin 3, type 1, P-cadherin NO exonic (placental) 106 2685306 5 6.33 Protein S (alpha) NO exonic 107 2526814 8 6.31 Fibronectin 1 NO antisense 108 2598344 7 6.3 Fibronectin 1 YES exonic 109 2491742 4 6.28 Surfactant, pulmonary-associated NO exonic protein B 110 2598318 11 6.28 Fibronectin 1 YES exonic 111 3839605 3 6.27 Kallikrein-related peptidase 10 NO exonic 112 2598325 11 6.27 Fibronectin 1 YES exonic 113 2451595 13 6.27 Chitinase 3-like 1 (cartilage NO exonic glycoprotein-39) 114 2721980 3 6.24 Solute carrier family 34 (sodium YES exonic phosphate), member 2 115 2721989 5 6.24 Solute carrier family 34 (sodium YES exonic phosphate), member 2 116 2722000 9 6.24 Solute carrier family 34 (sodium NO exonic phosphate), member 2 117 3087187 4 6.23 Tumor suppressor candidate 3 YES exonic 118 2526818 5 6.22 Fibronectin 1 NO antisense 119 2598331 3 6.21 Fibronectin 1 YES exonic 120 2721991 18 6.21 Solute carrier family 34 (sodium YES exonic phosphate), member 2 121 2400180 6 6.2 Calcium/calmodulin-dependent NO exonic protein kinase II inhibitor 1 122 2598287 5 6.19 Fibronectin 1 YES intronic 123 2584026 4 6.18 Dipeptidyl-peptidase 4 (CD26, YES exonic adenosine deaminase complexing protein 2) 124 3536744 6 6.17 Lectin, galactoside-binding, soluble, 3 YES exonic 125 2598284 8 6.17 Fibronectin 1 YES exonic 126 2598294 4 6.15 Fibronectin 1 YES exonic 127 3451875 3 6.14 NEL-like 2 (chicken) YES exonic 128 2598308 11 6.14 Fibronectin 1 YES exonic 129 3825011 5 6.08 Growth differentiation factor 15 NO exonic 130 3464873 4 6.07 Dual specificity phosphatase 6 NO exonic 131 3263765 5 6.07 Dual specificity phosphatase 5 NO intronic 132 2562440 30 6.07 Surfactant, pulmonary-associated NO intronic protein B 133 3536745 7 5.99 Lectin, galactoside-binding, soluble, 3 YES exonic 134 2598290 10 5.98 Fibronectin 1 YES exonic 135 2526822 14 5.98 Fibronectin 1 NO antisense 136 2526816 7 5.94 Fibronectin 1 NO antisense 137 2584027 4 5.92 Dipeptidyl-peptidase 4 (CD26, YES exonic adenosine deaminase complexing protein 2) 138 3329530 4 5.91 Low density lipoprotein receptor- NO exonic related protein 4 139 3020368 2 5.88 Met proto-oncogene (hepatocyte NO intronic growth factor receptor) 140 3451912 5 5.86 NEL-like 2 (chicken) YES exonic 141 3917691 4 5.83 T-cell lymphoma invasion and NO intronic metastasis 1 142 3420343 10 5.82 High mobility group AT-hook 2 NO intronic 143 3343460 3 5.81 Protease, serine, 23 NO exonic 144 2721982 8 5.8 Solute carrier family 34 (sodium YES exonic phosphate), member 2 145 2598310 9 5.79 Fibronectin 1 YES exonic 146 3270278 7 5.76 Protein tyrosine phosphatase, NO intronic receptor type, E 147 3577627 2 5.75 Serpin peptidase inhibitor, clade A YES exonic (alpha-1 antiproteinase, antitrypsin), member 1 148 2598276 10 5.74 Fibronectin 1 YES exonic 149 3907236 4 5.71 Syndecan 4 YES exonic 150 3464882 3 5.69 Dual specificity phosphatase 6 NO extra- genic 151 2598295 4 5.68 Fibronectin 1 NO intronic 152 2477461 4 5.67 Glutaminyl-peptide cyclotransferase NO exonic (glutaminyl cyclase) 153 2562463 10 5.67 Surfactant, pulmonary-associated YES exonic protein B 154 3907239 4 5.66 Syndecan 4 YES exonic 155 2944958 3 5.65 SRY (sex determining region Y)-box 4 NO antisense 156 2512878 8 5.65 Dipeptidyl-peptidase 4 (CD26, NO exonic adenosine deaminase complexing protein 2) 157 3020350 3 5.64 Met proto-oncogene (hepatocyte YES exonic growth factor receptor) 158 3928670 6 5.64 T-cell lymphoma invasion and NO intronic metastasis 1 159 3460452 4 5.61 High mobility group AT-hook 2 NO antisense 160 2721987 5 5.61 Solute carrier family 34 (sodium NO intronic phosphate), member 2 161 2598357 9 5.61 Fibronectin 1 YES exonic 162 2722004 12 5.59 Solute carrier family 34 (sodium NO exonic phosphate), member 2 163 3420360 6 5.58 High mobility group AT-hook 2 NO intronic 164 2721988 6 5.54 Solute carrier family 34 (sodium YES exonic phosphate), member 2 165 2721976 8 5.54 Solute carrier family 34 (sodium YES exonic phosphate), member 2 166 3634817 4 5.5 Cathepsin H NO exonic 167 2584025 4 5.5 Dipeptidyl-peptidase 4 (CD26, NO exonic adenosine deaminase complexing protein 2) 168 2722002 3 5.47 Solute carrier family 34 (sodium NO exonic phosphate), member 2 169 3312464 7 5.47 Protein tyrosine phosphatase, NO intronic receptor type, E 170 3263773 5 5.44 Dual specificity phosphatase 5 NO exonic 171 3460456 5 5.43 High mobility group AT-hook 2 NO antisense 172 2598373 15 5.41 Fibronectin 1 YES exonic 173 3730395 3 5.39 Mannose receptor, C type 2 NO exonic 174 2377256 3 5.38 CD55 molecule, decay accelerating NO exonic factor for complement (Cromer blood group) 175 3312484 5 5.38 Protein tyrosine phosphatase, NO exonic receptor type, E 176 3451913 7 5.38 NEL-like 2 (chicken) NO extra- genic 177 2400178 7 5.38 Calcium/calmodulin-dependent NO exonic protein kinase II inhibitor 1 178 2451596 20 5.38 Chitinase 3-like 1 (cartilage NO exonic glycoprotein-39) 179 2827686 7 5.37 Solute carrier family 27 (fatty acid YES exonic transporter), member 6 180 2685329 6 5.34 Protein S (alpha) NO intronic 181 2584058 9 5.33 Dipeptidyl-peptidase 4 (CD26, YES exonic adenosine deaminase complexing protein 2) 182 2598306 8 5.32 Fibronectin 1 YES exonic 183 3928671 2 5.3 T-cell lymphoma invasion and NO intronic metastasis 1 184 3069235 2 5.3 Met proto-oncogene (hepatocyte NO exonic growth factor receptor) 185 2562462 3 5.29 Surfactant, pulmonary-associated NO intronic protein B 186 2632440 4 5.29 Protein S (alpha) NO exonic 187 3420356 4 5.29 High mobility group AT-hook 2 NO intronic 188 3263784 4 5.24 Dual specificity phosphatase 5 NO intronic 189 3769232 3 5.23 Potassium inwardly-rectifying NO exonic channel, subfamily J, member 2 190 2377249 4 5.23 CD55 molecule, decay accelerating NO intronic factor for complement (Cromer blood group) 191 2377248 3 5.18 CD55 molecule, decay accelerating NO exonic factor for complement (Cromer blood group) 192 3067479 3 5.17 Neuronal cell adhesion molecule NO exonic 193 3020359 6 5.16 Met proto-oncogene (hepatocyte NO intronic growth factor receptor) 194 3451874 7 5.16 NEL-like 2 (chicken) YES exonic 195 2491744 9 5.16 Surfactant, pulmonary-associated NO intronic protein B 196 3464875 3 5.08 Dual specificity phosphatase 6 NO intronic 197 2584067 3 5.06 Dipeptidyl-peptidase 4 (CD26, YES exonic adenosine deaminase complexing protein 2) 198 2722005 10 5.06 Solute carrier family 34 (sodium NO exonic phosphate), member 2 199 3721357 7 5.05 Keratin 19 NO antisense 200 3020391 3 5.04 Met proto-oncogene (hepatocyte YES exonic growth factor receptor) 201 3868840 5 5.04 Kallikrein-related peptidase 10 NO extra- genic 202 3306643 3 5.02 Dual specificity phosphatase 5 NO extra- genic 203 2721994 3 5.02 Solute carrier family 34 (sodium YES exonic phosphate), member 2 204 3329532 3 5.01 Low density lipoprotein receptor- NO antisense related protein 4 205 2377247 3 5 CD55 molecule, decay accelerating YES exonic factor for complement (Cromer blood group) 206 2558642 3 5 Transforming growth factor, alpha NO exonic 207 2830378 2 4.99 Sparc/osteonectin, cwcv and kazal- NO antisense like domains proteoglycan (testican) 1 208 2477456 4 4.98 Glutaminyl-peptide cyclotransferase YES exonic (glutaminyl cyclase) 209 3451868 6 4.98 NEL-like 2 (chicken) YES exonic 210 3087225 6 4.98 Tumor suppressor candidate 3 NO extra- genic 211 3020384 3 4.89 Met proto-oncogene (hepatocyte YES exonic growth factor receptor) 212 2735072 5 4.87 Secreted phosphoprotein 1 NO exonic (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1) 213 2598353 8 4.87 Fibronectin 1 YES exonic 214 3385809 3 4.85 Cathepsin C NO intronic 215 2721985 9 4.85 Solute carrier family 34 (sodium YES exonic phosphate), member 2 216 3020363 4 4.77 Met proto-oncogene (hepatocyte YES exonic growth factor receptor) 217 2437139 3 4.76 Mucin 1, cell surface associated YES intronic 218 3460453 3 4.72 High mobility group AT-hook 2 NO antisense 219 3020356 3 4.72 Met proto-oncogene (hepatocyte NO intronic growth factor receptor) 220 2830183 9 4.72 Sparc/osteonectin, cwcv and kazal- NO exonic like domains proteoglycan (testican) 1 221 2562444 2 4.7 Surfactant, pulmonary-associated NO intronic protein B 222 2830181 4 4.68 Sparc/osteonectin, cwcv and kazal- NO exonic like domains proteoglycan (testican) 1 223 2764175 13 4.68 Solute carrier family 34 (sodium NO exonic phosphate), member 2 224 3907240 3 4.66 Syndecan 4 YES exonic 225 3917721 5 4.66 T-cell lymphoma invasion and NO intronic metastasis 1 226 2442021 9 4.65 Retinoid X receptor, gamma YES exonic 227 3907235 3 4.64 Syndecan 4 NO exonic 228 3451838 3 4.64 NEL-like 2 (chicken) YES exonic 229 3769234 5 4.64 Potassium inwardly-rectifying NO exonic channel, subfamily J, member 2 230 3577636 4 4.63 Serpin peptidase inhibitor, clade A YES exonic (alpha-1 antiproteinase, antitrypsin), member 1 231 2377236 3 4.61 CD55 molecule, decay accelerating YES exonic factor for complement (Cromer blood group) 232 2435391 4 4.61 S100 calcium binding protein A10 NO exonic 233 2400186 3 4.6 Calcium/calmodulin-dependent NO extra- protein kinase II inhibitor 1 genic 234 2751991 3 4.59 UDP-N-acetyl-alpha-D- NO intronic galactosamine: polypeptide N- acetylgalactosaminyltransferase 7 (GalNAc-T7) 235 2377265 3 4.53 CD55 molecule, decay accelerating NO intronic factor for complement (Cromer blood group) 236 3464878 4 4.53 Dual specificity phosphatase 6 NO intronic 237 3835890 6 4.53 Apolipoprotein E NO exonic 238 2721999 15 4.53 Solute carrier family 34 (sodium NO exonic phosphate), member 2 239 3125505 3 4.5 Tumor suppressor candidate 3 NO antisense 240 2562443 5 4.5 Surfactant, pulmonary-associated NO intronic protein B 241 2453163 3 4.45 CD55 molecule, decay accelerating NO antisense factor for complement (Cromer blood group) 242 3263752 6 4.43 Dual specificity phosphatase 5 NO extra- genic 243 3825012 3 4.4 Growth differentiation factor 15 NO exonic 244 2377237 3 4.39 CD55 molecule, decay accelerating NO intronic factor for complement (Cromer blood group) 245 3907194 6 4.39 Secretory leukocyte peptidase NO exonic inhibitor 246 2377268 3 4.36 CD55 molecule, decay accelerating YES exonic factor for complement (Cromer blood group) 247 3343458 3 4.36 Protease, serine, 23 NO exonic 248 2377271 4 4.36 CD55 molecule, decay accelerating NO exonic factor for complement (Cromer blood group) 249 2359172 5 4.36 S100 calcium binding protein A10 NO exonic 250 3733280 3 4.35 Potassium inwardly-rectifying NO exonic channel, subfamily J, member 2 251 3020355 3 4.34 Met proto-oncogene (hepatocyte NO intronic growth factor receptor) 252 2453796 3 4.32 Laminin, beta 3 YES exonic 253 3460455 3 4.31 High mobility group AT-hook 2 NO antisense 254 3928709 2 4.3 T-cell lymphoma invasion and YES exonic metastasis 1 255 3351275 3 4.3 Myelin protein zero-like 2 NO exonic 256 3217301 2 4.21 Gamma-aminobutyric acid (GABA) B NO intronic receptor, 2 257 2377263 2 4.18 CD55 molecule, decay accelerating NO intronic factor for complement (Cromer blood group) 258 3976345 3 4.18 Synapsin I YES antisense 259 2377245 5 4.16 CD55 molecule, decay accelerating YES exonic factor for complement (Cromer blood group) 260 2414961 4 4.14 Tumor-associated calcium signal NO exonic transducer 2 261 2876911 2 4.12 Sparc/osteonectin, cwcv and kazal- YES exonic like domains proteoglycan (testican) 1 262 2764272 4 4.12 KIAA0746 protein YES exonic 263 3406425 3 4.08 Epidermal growth factor receptor NO intronic pathway substrate 8 264 3222204 7 4.06 Tenascin C (hexabrachion) NO intronic 265 3928687 3 4.05 T-cell lymphoma invasion and YES exonic metastasis 1 266 3067480 4 4.04 Neuronal cell adhesion molecule NO exonic 267 3451908 7 3.99 NEL-like 2 (chicken) YES exonic 268 3928672 5 3.98 T-cell lymphoma invasion and NO intronic metastasis 1 269 2437119 4 3.97 Mucin 1, cell surface associated NO exonic 270 3252064 3 3.93 Plasminogen activator, urokinase NO exonic 271 2751985 2 3.92 UDP-N-acetyl-alpha-D- NO exonic galactosamine: polypeptide N- acetylgalactosaminyltransferase 7 (GalNAc-T7) 272 2830201 2 3.9 Sparc/osteonectin, cwcv and kazal- NO antisense like domains proteoglycan (testican) 1 273 3126229 3 3.9 Pleckstrin and Sec7 domain NO intronic containing 3 274 3252057 2 3.83 Plasminogen activator, urokinase NO intronic 275 3222218 3 3.82 Tenascin C (hexabrachion) NO intronic 276 3217284 3 3.8 Gamma-aminobutyric acid (GABA) B YES exonic receptor, 2 277 3401511 3 3.76 Protein arginine methyltransferase 8 NO exonic 278 3020380 3 3.74 Met proto-oncogene (hepatocyte YES exonic growth factor receptor) 279 3371817 2 3.71 Low density lipoprotein receptor- NO exonic related protein 4 280 2377242 2 3.7 CD55 molecule, decay accelerating YES exonic factor for complement (Cromer blood group) 281 2558708 2 3.7 Transforming growth factor, alpha NO intronic 282 3351271 2 3.63 Myelin protein zero-like 3 NO antisense 283 2466609 −8 −10.87 Thyroid peroxidase YES exonic 284 2537610 −6 −9.87 Thyroid peroxidase NO antisense 285 3727592 −3 −9.81 Hepatic leukemia factor YES exonic 286 3367674 −11 −9.45 Metallophosphoesterase domain NO extra- containing 2 genic 287 2537568 −19 −9.37 Thyroid peroxidase NO antisense 288 2466585 −26 −9.14 Thyroid peroxidase YES exonic 289 3095405 −9 −9.07 Zinc finger, matrin type 4 NO exonic 290 3933538 −16 −8.91 Trefoil factor 3 (intestinal) NO exonic 291 2763591 −3 −8.86 Peroxisome proliferator-activated YES exonic receptor gamma, coactivator 1 alpha 292 2466562 −30 −8.84 Thyroid peroxidase NO extra- genic 293 3662208 −6 −8.84 Metallothionein 1F YES exonic 294 2466570 −55 −8.64 Thyroid peroxidase YES exonic 295 2466631 −19 −8.52 Thyroid peroxidase NO extra- genic 296 3367675 −14 −8.49 Metallophosphoesterase domain NO exonic containing 2 297 3367709 −24 −8.39 Metallophosphoesterase domain NO intronic containing 2 298 2466600 −5 −8.37 Thyroid peroxidase NO intronic 299 3367737 −9 −8.35 Metallophosphoesterase domain NO intronic containing 2 300 3018629 −8 −8.35 Solute carrier family 26, member 4 YES exonic 301 3132624 −6 −8.35 Zinc finger, matrin type 4 YES exonic 302 3018633 −7 −8.33 Solute carrier family 26, member 4 YES exonic 303 3367689 −13 −8.27 Metallophosphoesterase domain NO intronic containing 2 304 3693001 −14 −8.22 Metallothionein 1G NO exonic 305 3018617 −6 −8.21 Solute carrier family 26, member 4 YES exonic 306 3018618 −8 −8.16 Solute carrier family 26, member 4 YES exonic 307 2763563 −6 −8.12 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 308 3862190 −3 −8.03 Fc fragment of IgG binding protein NO exonic 309 2721305 −4 −8 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 310 2466569 −26 −7.98 Thyroid peroxidase YES exonic 311 3367712 −15 −7.98 Metallophosphoesterase domain YES exonic containing 2 312 3833280 −2 −7.97 Fc fragment of IgG binding protein NO exonic 313 3018642 −10 −7.93 Solute carrier family 26, member 4 NO exonic 314 3367697 −16 −7.85 Metallophosphoesterase domain NO intronic containing 2 315 3018616 −13 −7.79 Solute carrier family 26, member 4 YES exonic 316 3367743 −28 −7.77 Metallophosphoesterase domain NO extra- containing 2 genic 317 2537609 −12 −7.73 Thyroid peroxidase NO antisense 318 3933539 −19 −7.71 Trefoil factor 3 (intestinal) NO exonic 319 2586066 −4 −7.7 Low density lipoprotein-related YES exonic protein 2 320 3132617 −13 −7.68 Zinc finger, matrin type 4 NO extra- genic 321 2727647 −11 −7.6 V-kit Hardy-Zuckerman 4 feline NO intronic sarcoma viral oncogene homolog 322 3067233 −20 −7.53 Solute carrier family 26, member 4 NO extra- genic 323 2763565 −5 −7.53 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 324 3018631 −13 −7.51 Solute carrier family 26, member 4 YES exonic 325 2586118 −7 −7.5 Low density lipoprotein-related YES exonic protein 2 326 2466608 −9 −7.49 Thyroid peroxidase YES exonic 327 2586111 −5 −7.46 Low density lipoprotein-related YES exonic protein 2 328 3367703 −9 −7.44 Metallophosphoesterase domain NO intronic containing 2 329 2586171 −4 −7.36 Low density lipoprotein-related YES exonic protein 2 330 2331562 −3 −7.34 Bone morphogenetic protein 8a NO exonic 331 3108614 −4 −7.32 Matrilin 2 YES exonic 332 2537604 −6 −7.31 Thyroid peroxidase NO antisense 333 2466626 −3 −7.31 Thyroid peroxidase YES exonic 334 3018640 −7 −7.28 Solute carrier family 26, member 4 NO exonic 335 2466621 −14 −7.27 Thyroid peroxidase NO intronic 336 2586151 −3 −7.27 Low density lipoprotein-related YES exonic protein 2 337 2466629 −16 −7.25 Thyroid peroxidase NO exonic 338 3367739 −8 −7.24 Metallophosphoesterase domain NO intronic containing 2 339 3811356 −3 −7.22 B-cell CLL/lymphoma 2 NO exonic 340 3108604 −3 −7.19 Matrilin 2 YES exonic 341 2466601 −35 −7.17 Thyroid peroxidase NO intronic 342 2466566 −9 −7.16 Thyroid peroxidase YES exonic 343 2586065 −5 −7.14 Low density lipoprotein-related YES exonic protein 2 344 3095521 −5 −7.14 Zinc finger, matrin type 4 NO intronic 345 2586168 −4 −7.14 Low density lipoprotein-related YES exonic protein 2 346 2466628 −21 −7.13 Thyroid peroxidase YES exonic 347 2466564 −13 −7.1 Thyroid peroxidase YES exonic 348 3160700 −5 −7.04 Chromosome 9 open reading frame 68 NO intronic 349 3325106 −9 −7 Metallophosphoesterase domain NO antisense containing 2 350 2721311 −7 −6.99 Peroxisome proliferator-activated NO antisense receptor gamma, coactivator 1 alpha 351 3018619 −6 −6.98 Solute carrier family 26, member 4 YES exonic 352 2586130 −6 −6.9 Low density lipoprotein-related YES exonic protein 2 353 3018636 −12 −6.87 Solute carrier family 26, member 4 YES exonic 354 3108616 −8 −6.86 Matrilin 2 YES exonic 355 3862193 −4 −6.86 Fc fragment of IgG binding protein YES exonic 356 2586039 −4 −6.84 Low density lipoprotein-related NO extra- protein 2 genic 357 3833286 −4 −6.83 Fc fragment of IgG binding protein NO antisense 358 2586132 −2 −6.83 Low density lipoprotein-related YES exonic protein 2 359 2763561 −6 −6.82 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 360 2466620 −15 −6.8 Thyroid peroxidase YES exonic 361 2586145 −5 −6.79 Low density lipoprotein-related YES exonic protein 2 362 3018615 −7 −6.78 Solute carrier family 26, member 4 YES exonic 363 4023363 −6 −6.77 Four and a half LIM domains 1 NO exonic 364 2537578 −6 −6.76 Thyroid peroxidase NO antisense 365 2763557 −6 −6.74 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 366 2586159 −4 −6.74 Low density lipoprotein-related YES exonic protein 2 367 3862199 −4 −6.74 Fc fragment of IgG binding protein YES exonic 368 3811391 −2 −6.74 B-cell CLL/lymphoma 2 NO intronic 369 3046252 −4 −6.73 Engulfment and cell motility 1 NO intronic 370 2466563 −7 −6.72 Thyroid peroxidase NO extra- genic 371 2721641 −5 −6.72 Superoxide dismutase 3, extracellular NO exonic 372 3108607 −3 −6.71 Matrilin 2 NO intronic 373 2721309 −5 −6.7 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 374 2586135 −4 −6.7 Low density lipoprotein-related YES exonic protein 2 375 2537607 −9 −6.69 Thyroid peroxidase NO antisense 376 3018628 −8 −6.67 Solute carrier family 26, member 4 YES exonic 377 2466583 −8 −6.66 Thyroid peroxidase NO intronic 378 2586149 −5 −6.65 Low density lipoprotein-related YES exonic protein 2 379 3662230 −16 −6.63 Metallothionein 1G NO exonic 380 2586140 −3 −6.61 Low density lipoprotein-related YES exonic protein 2 381 3367740 −6 −6.53 Metallophosphoesterase domain NO intronic containing 2 382 2466598 −5 −6.53 Thyroid peroxidase NO intronic 383 3367691 −6 −6.52 Metallophosphoesterase domain NO intronic containing 2 384 3367679 −4 −6.49 Metallophosphoesterase domain YES exonic containing 2 385 2763590 −3 −6.47 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 386 2586102 −3 −6.46 Low density lipoprotein-related YES exonic protein 2 387 3367693 −12 −6.44 Metallophosphoesterase domain NO intronic containing 2 388 2586067 −4 −6.44 Low density lipoprotein-related YES exonic protein 2 389 3095516 −3 −6.42 Zinc finger, matrin type 4 NO intronic 390 2466573 −10 −6.41 Thyroid peroxidase YES intronic 391 3145950 −5 −6.41 Matrilin 2 NO antisense 392 2586043 −3 −6.41 Low density lipoprotein-related YES exonic protein 2 393 3104982 −23 −6.37 Fatty acid binding protein 4, adipocyte NO exonic 394 2336909 −7 −6.37 Deiodinase, iodothyronine, type I NO exonic 395 3108620 −6 −6.37 Matrilin 2 NO exonic 396 3095473 −4 −6.31 Zinc finger, matrin type 4 NO antisense 397 2763580 −4 −6.31 Peroxisome proliferator-activated YES exonic receptor gamma, coactivator 1 alpha 398 3367742 −3 −6.31 Metallophosphoesterase domain NO extra- containing 2 genic 399 3662219 −17 −6.29 Metallothionein 1H NO exonic 400 2331563 −11 −6.29 Bone morphogenetic protein 8a NO exonic 401 3727624 −9 −6.29 Hepatic leukemia factor NO exonic 402 2586061 −4 −6.29 Low density lipoprotein-related NO intronic protein 2 403 2997517 −4 −6.28 Engulfment and cell motility 1 NO exonic 404 2586088 −3 −6.28 Low density lipoprotein-related YES exonic protein 2 405 3992449 −5 −6.27 Four and a half LIM domains 1 NO exonic 406 2586155 −9 −6.25 Low density lipoprotein-related YES exonic protein 2 407 2586139 −4 −6.25 Low density lipoprotein-related YES exonic protein 2 408 3126609 −3 −6.25 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 409 2586113 −4 −6.2 Low density lipoprotein-related YES exonic protein 2 410 2586160 −4 −6.2 Low density lipoprotein-related YES exonic protein 2 411 2608611 −3 −6.2 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 412 2586152 −3 −6.19 Low density lipoprotein-related YES exonic protein 2 413 2466574 −10 −6.17 Thyroid peroxidase NO intronic 414 2763558 −4 −6.14 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 415 3662224 −4 −6.13 Metallothionein 1F NO extra- genic 416 3367741 −3 −6.13 Metallophosphoesterase domain YES exonic containing 2 417 2466582 −3 −6.12 Thyroid peroxidase YES exonic 418 3108615 −5 −6.08 Matrilin 2 YES exonic 419 2721643 −2 −6.08 Superoxide dismutase 3, extracellular NO exonic 420 2586153 −4 −6.07 Low density lipoprotein-related YES exonic protein 2 421 2537572 −8 −6.05 Thyroid peroxidase NO antisense 422 2586068 −3 −6.04 Low density lipoprotein-related YES exonic protein 2 423 2586143 −5 −6.03 Low density lipoprotein-related YES exonic protein 2 424 3893028 −15 −6.01 Collagen, type IX, alpha 3 YES exonic 425 3367676 −7 −6.01 Metallophosphoesterase domain YES exonic containing 2 426 2586172 −5 −6.01 Low density lipoprotein-related YES exonic protein 2 427 3142383 −25 −6 Fatty acid binding protein 4, NO exonic adipocyte 428 3367677 −3 −5.97 Metallophosphoesterase domain NO intronic containing 2 429 2763566 −7 −5.96 Peroxisome proliferator-activated NO exonic receptor gamma, coactivator 1 alpha 430 3763228 −5 −5.94 Hepatic leukemia factor NO extra- genic 431 3108580 −4 −5.92 Matrilin 2 YES exonic 432 2608485 −3 −5.92 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 433 2331560 −3 −5.91 Bone morphogenetic protein 8a YES exonic 434 2763602 −3 −5.89 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 435 3811360 −2 −5.89 B-cell CLL/lymphoma 2 NO exonic 436 2586150 −4 −5.86 Low density lipoprotein-related YES exonic protein 2 437 2586050 −7 −5.84 Low density lipoprotein-related YES exonic protein 2 438 2586041 −3 −5.84 Low density lipoprotein-related YES exonic protein 2 439 3811354 −3 −5.84 B-cell CLL/lymphoma 2 NO exonic 440 3088273 −5 −5.82 Chondroitin beta1,4 N- NO exonic acetylgalactosaminyltransferase 441 3046251 −3 −5.81 Engulfment and cell motility 1 NO intronic 442 2336897 −5 −5.8 Deiodinase, iodothyronine, type I YES exonic 443 3108574 −5 −5.8 Matrilin 2 YES exonic 444 3354771 −2 −5.8 STT3, subunit of the YES exonic oligosaccharyltransferase complex, homolog A (S. cerevisiae) 445 3367705 −7 −5.77 Metallophosphoesterase domain NO intronic containing 2 446 3791751 −5 −5.77 B-cell CLL/lymphoma 2 NO antisense 447 2514350 −4 −5.77 Low density lipoprotein-related NO extra- protein 2 genic 448 2586107 −5 −5.75 Low density lipoprotein-related YES exonic protein 2 449 3108602 −9 −5.74 Matrilin 2 YES exonic 450 2980482 −4 −5.73 Opioid receptor, mu 1 NO antisense 451 3126512 −3 −5.72 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 452 3913484 −22 −5.71 Collagen, type IX, alpha 3 NO exonic 453 2763571 −4 −5.71 Peroxisome proliferator-activated YES exonic receptor gamma, coactivator 1 alpha 454 2980481 −10 −5.7 Opioid receptor, mu 1 NO antisense 455 2608604 −6 −5.68 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 456 2586064 −6 −5.66 Low density lipoprotein-related YES exonic protein 2 457 3018621 −4 −5.66 Solute carrier family 26, member 4 YES exonic 458 3126626 −4 −5.66 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 459 2649002 −6 −5.65 Potassium voltage-gated channel, NO intronic shaker-related subfamily, beta member 1 460 2763573 −4 −5.65 Peroxisome proliferator-activated YES exonic receptor gamma, coactivator 1 alpha 461 3396651 −2 −5.65 STT3, subunit of the NO antisense oligosaccharyltransferase complex, homolog A (S. cerevisiae) 462 3893026 −12 −5.64 Collagen, type IX, alpha 3 YES exonic 463 3046250 −4 −5.64 Engulfment and cell motility 1 NO intronic 464 2586114 −3 −5.62 Low density lipoprotein-related YES exonic protein 2 465 3573880 −6 −5.61 Deiodinase, iodothyronine, type II YES exonic 466 2586101 −4 −5.61 Low density lipoprotein-related YES exonic protein 2 467 3126515 −3 −5.57 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 468 2608689 −3 −5.57 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 469 4023359 −4 −5.55 Four and a half LIM domains 1 NO exonic 470 3046202 −2 −5.55 Engulfment and cell motility 1 YES exonic 471 2586075 −5 −5.53 Low density lipoprotein-related YES exonic protein 2 472 3046247 −6 −5.52 Engulfment and cell motility 1 NO intronic 473 2608629 −2 −5.51 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 474 3126548 −3 −5.49 Chondroitin beta1,4 N- YES exonic acetylgalactosaminyltransferase 475 3046198 −3 −5.46 Engulfment and cell motility 1 NO exonic 476 3573882 −3 −5.45 Deiodinase, iodothyronine, type II YES exonic 477 3088320 −3 −5.43 Chondroitin beta1,4 N- NO antisense acetylgalactosaminyltransferase 478 3095470 −2 −5.43 Zinc finger, matrin type 4 NO antisense 479 3142384 −13 −5.42 Fatty acid binding protein 4, YES exonic adipocyte 480 2721315 −5 −5.42 Peroxisome proliferator-activated NO antisense receptor gamma, coactivator 1 alpha 481 3933546 −3 −5.42 Trefoil factor 3 (intestinal) YES exonic 482 2608677 −3 −5.42 Inositol 1,4,5-triphosphate receptor, NO exonic type 1 483 3893027 −8 −5.41 Collagen, type IX, alpha 3 YES exonic 484 3811420 −4 −5.41 B-cell CLL/lymphoma 2 NO intronic 485 2608644 −3 −5.41 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 486 3811357 −2 −5.41 B-cell CLL/lymphoma 2 NO exonic 487 2932274 −6 −5.38 Opioid receptor, mu 1 YES exonic 488 3811424 −3 −5.37 B-cell CLL/lymphoma 2 NO intronic 489 3354786 −2 −5.37 STT3, subunit of the YES exonic oligosaccharyltransferase complex, homolog A (S. cerevisiae) 490 2740186 −5 −5.35 Ankyrin 2, neuronal NO intronic 491 2586104 −3 −5.33 Low density lipoprotein-related NO intronic protein 2 492 3046236 −5 −5.32 Engulfment and cell motility 1 NO intronic 493 3126509 −4 −5.31 Chondroitin beta1,4 N- YES exonic acetylgalactosaminyltransferase 494 2608546 −4 −5.3 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 495 3108613 −5 −5.29 Matrilin 2 YES exonic 496 2997541 −3 −5.29 Engulfment and cell motility 1 NO antisense 497 3088362 −2 −5.29 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 498 3662157 −5 −5.28 Metallothionein 1M NO exonic 499 2586125 −3 −5.28 Low density lipoprotein-related NO intronic protein 2 500 2608612 −7 −5.27 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 501 2608627 −7 −5.25 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 502 2586096 −5 −5.25 Low density lipoprotein-related YES exonic protein 2 503 3811405 −3 −5.25 B-cell CLL/lymphoma 2 NO intronic 504 3862194 −3 −5.25 Fc fragment of IgG binding protein NO intronic 505 3126514 −4 −5.24 Chondroitin beta1,4 N- YES exonic acetylgalactosaminyltransferase 506 3992437 −4 −5.23 Four and a half LIM domains 1 NO intronic 507 2702151 −3 −5.22 Potassium voltage-gated channel, NO exonic shaker-related subfamily, beta member 1 508 2649018 −7 −5.21 Potassium voltage-gated channel, NO intronic shaker-related subfamily, beta member 1 509 2908192 −2 −5.19 Vascular endothelial growth factor A YES exonic 510 3573883 −4 −5.18 Deiodinase, iodothyronine, type II YES exonic 511 2608610 −4 −5.17 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 512 3126563 −3 −5.17 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 513 2897465 −2 −5.17 Inhibitor of DNA binding 4, dominant NO exonic negative helix-loop-helix protein 514 2649054 −6 −5.16 Potassium voltage-gated channel, NO intronic shaker-related subfamily, beta member 1 515 2608569 −3 −5.16 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 516 2897464 −2 −5.16 Inhibitor of DNA binding 4, dominant NO exonic negative helix-loop-helix protein 517 2980483 −3 −5.14 Opioid receptor, mu 1 NO antisense 518 3791695 −3 −5.14 B-cell CLL/lymphoma 2 NO antisense 519 3603305 −10 −5.13 Cellular retinoic acid binding protein 1 NO exonic 520 2608568 −4 −5.11 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 521 2586115 −3 −5.11 Low density lipoprotein-related YES exonic protein 2 522 3811408 −2 −5.11 B-cell CLL/lymphoma 2 NO intronic 523 2466604 −2 −5.11 Thyroid peroxidase YES exonic 524 2586166 −3 −5.09 Low density lipoprotein-related YES exonic protein 2 525 3018611 −4 −5.08 Solute carrier family 26, member 4 NO extra- genic 526 3693008 −4 −5.07 Metallothionein 1G YES exonic 527 3862192 −4 −5.07 Fc fragment of IgG binding protein YES exonic 528 2649046 −3 −5.07 Potassium voltage-gated channel, YES exonic shaker-related subfamily, beta member 1 529 2763600 −4 −5.05 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 530 3546098 −3 −5.05 Deiodinase, iodothyronine, type II NO exonic 531 3385513 −2 −5.05 Frizzled homolog 4 (Drosophila) NO exonic 532 2586141 −3 −5.02 Low density lipoprotein-related YES exonic protein 2 533 3046243 −5 −5 Engulfment and cell motility 1 NO intronic 534 3046224 −2 −5 Engulfment and cell motility 1 YES exonic 535 4023361 −2 −5 Four and a half LIM domains 1 NO exonic 536 3546096 −5 −4.99 Deiodinase, iodothyronine, type II NO antisense 537 3046199 −5 −4.98 Engulfment and cell motility 1 YES exonic 538 2740233 −4 −4.97 Ankyrin 2, neuronal NO intronic 539 2980484 −9 −4.96 Opioid receptor, mu 1 NO antisense 540 2980450 −7 −4.96 Opioid receptor, mu 1 YES antisense 541 2763628 −3 −4.96 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 542 2649009 −4 −4.95 Potassium voltage-gated channel, NO intronic shaker-related subfamily, beta member 1 543 3791690 −4 −4.94 B-cell CLL/lymphoma 2 NO antisense 544 2980485 −7 −4.93 Opioid receptor, mu 1 YES antisense 545 3811446 −3 −4.9 B-cell CLL/lymphoma 2 NO extra- genic 546 3811358 −3 −4.89 B-cell CLL/lymphoma 2 NO exonic 547 3573884 −8 −4.88 Deiodinase, iodothyronine, type II YES exonic 548 2925965 −5 −4.88 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 549 2608614 −5 −4.87 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 550 3787876 −3 −4.86 Lipase, endothelial YES exonic 551 2331561 −4 −4.85 Bone morphogenetic protein 8a NO exonic 552 2661102 −2 −4.85 Inositol 1,4,5-triphosphate receptor, NO antisense type 1 553 2586047 −3 −4.84 Low density lipoprotein-related YES exonic protein 2 554 3787860 −2 −4.84 Lipase, endothelial YES exonic 555 3791668 −2 −4.84 B-cell CLL/lymphoma 2 NO antisense 556 3126507 −3 −4.83 Chondroitin beta1,4 N- NO exonic acetylgalactosaminyltransferase 557 2608643 −3 −4.82 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 558 3807530 −2 −4.82 Lipase, endothelial NO antisense 559 2763570 −4 −4.81 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 560 2925993 −4 −4.81 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 561 2908205 −2 −4.81 Vascular endothelial growth factor A YES exonic 562 2586123 −4 −4.8 Low density lipoprotein-related YES exonic protein 2 563 3791749 −4 −4.79 B-cell CLL/lymphoma 2 NO antisense 564 2586195 −6 −4.78 Low density lipoprotein-related NO intronic protein 2 565 2763627 −3 −4.78 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 566 2608559 −4 −4.74 Inositol 1,4,5-triphosphate receptor, YES exonic type 1 567 3126535 −4 −4.74 Chondroitin beta1,4 N- YES exonic acetylgalactosaminyltransferase 568 3791743 −4 −4.74 B-cell CLL/lymphoma 2 NO antisense 569 3727623 −5 −4.73 Hepatic leukemia factor NO exonic 570 2586057 −5 −4.73 Low density lipoprotein-related YES exonic protein 2 571 2763598 −3 −4.73 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 572 2925966 −3 −4.72 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 573 4016044 −3 −4.7 Transcription elongation factor A NO antisense (SII)-like 2 574 2980475 −8 −4.69 Opioid receptor, mu 1 YES antisense 575 2944441 −2 −4.68 Inhibitor of DNA binding 4, dominant NO exonic negative helix-loop-helix protein 576 2608634 −3 −4.66 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 577 2727646 −6 −4.64 V-kit Hardy-Zuckerman 4 feline NO exonic sarcoma viral oncogene homolog 578 2925978 −3 −4.63 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 579 2608490 −3 −4.58 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 580 3992448 −3 −4.58 Four and a half LIM domains 1 YES exonic 581 3985030 −4 −4.57 Transcription elongation factor A YES exonic (SII)-like 2 582 3753968 −2 −4.57 Chemokine (C-C motif) ligand 14 NO exonic 583 3573878 −4 −4.56 Deiodinase, iodothyronine, type II YES exonic 584 2586112 −2 −4.56 Low density lipoprotein-related YES exonic protein 2 585 3354794 −2 −4.56 STT3, subunit of the NO extra- oligosaccharyltransferase complex, genic homolog A (S. cerevisiae) 586 2925981 −5 −4.55 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 587 3787884 −3 −4.55 Lipase, endothelial NO exonic 588 3791660 −2 −4.55 B-cell CLL/lymphoma 2 NO antisense 589 3569378 −2 −4.54 Arginase, type II YES exonic 590 2932285 −7 −4.53 Opioid receptor, mu 1 NO intronic 591 2586165 −2 −4.53 Low density lipoprotein-related NO intronic protein 2 592 3787881 −3 −4.52 Lipase, endothelial NO intronic 593 2925998 −5 −4.5 Ectonucleotide NO exonic pyrophosphatase/phosphodiesterase 1 594 2925975 −4 −4.5 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 595 3126541 −3 −4.5 Chondroitin beta1,4 N- NO intronic acetylgalactosaminyltransferase 596 2782529 −2 −4.5 Ankyrin 2, neuronal NO exonic 597 2763596 −3 −4.47 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 598 2649079 −5 −4.45 Potassium voltage-gated channel, NO exonic shaker-related subfamily, beta member 1 599 2925973 −2 −4.44 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 600 2925999 −4 −4.43 Ectonucleotide NO exonic pyrophosphatase/phosphodiesterase 1 601 3126508 −3 −4.43 Chondroitin beta1,4 N- NO exonic acetylgalactosaminyltransferase 602 2586072 −4 −4.42 Low density lipoprotein-related YES exonic protein 2 603 2661165 −3 −4.41 Inositol 1,4,5-triphosphate receptor, NO exonic type 1 604 3018623 −3 −4.4 Solute carrier family 26, member 4 YES exonic 605 2944443 −2 −4.4 Inhibitor of DNA binding 4, dominant NO extra- negative helix-loop-helix protein genic 606 3573881 −6 −4.38 Deiodinase, iodothyronine, type II YES exonic 607 3573895 −4 −4.38 Deiodinase, iodothyronine, type II YES exonic 608 3811395 −3 −4.38 B-cell CLL/lymphoma 2 NO intronic 609 3787879 −4 −4.36 Lipase, endothelial NO intronic 610 2608590 −4 −4.35 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 611 2763629 −5 −4.34 Peroxisome proliferator-activated NO intronic receptor gamma, coactivator 1 alpha 612 3992441 −4 −4.34 Four and a half LIM domains 1 NO intronic 613 3727625 −4 −4.33 Hepatic leukemia factor NO exonic 614 2615655 −2 −4.33 STT3, subunit of the NO extra- oligosaccharyltransferase complex, genic homolog B (S. cerevisiae) 615 3603302 −3 −4.27 Cellular retinoic acid binding protein 1 YES exonic 616 2586098 −3 −4.24 Low density lipoprotein-related NO intronic protein 2 617 2925996 −5 −4.22 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 618 3791650 −3 −4.21 B-cell CLL/lymphoma 2 NO exonic 619 2925967 −4 −4.19 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 620 2586147 −3 −4.17 Low density lipoprotein-related YES exonic protein 2 621 3992436 −4 −4.14 Four and a half LIM domains 1 NO intronic 622 2740285 −3 −4.13 Ankyrin 2, neuronal NO exonic 623 2740135 −2 −4.13 Ankyrin 2, neuronal NO intronic 624 2586097 −3 −4.08 Low density lipoprotein-related YES exonic protein 2 625 3985031 −3 −4.07 Transcription elongation factor A NO exonic (SII)-like 2 626 2954890 −2 −4.07 Vascular endothelial growth factor A NO antisense 627 2586154 −4 −4.06 Low density lipoprotein-related YES exonic protein 2 628 4013550 −2 −4.06 Integral membrane protein 2A NO exonic 629 3811377 −3 −4.03 B-cell CLL/lymphoma 2 NO intronic 630 3763258 −4 −3.99 Hepatic leukemia factor NO exonic 631 3753974 −2 −3.98 Chemokine (C-C motif) ligand 14 YES exonic 632 3088318 −2 −3.97 Chondroitin beta1,4 N- NO antisense acetylgalactosaminyltransferase 633 2925979 −4 −3.94 Ectonucleotide YES exonic pyrophosphatase/phosphodiesterase 1 634 3811413 −3 −3.94 B-cell CLL/lymphoma 2 NO intronic 635 2980497 −3 −3.91 Phosphoinositide-binding protein NO intronic PIP3-E 636 3811431 −4 −3.9 B-cell CLL/lymphoma 2 NO intronic 637 2608556 −3 −3.9 Inositol 1,4,5-triphosphate receptor, NO intronic type 1 638 3108611 −3 −3.9 Matrilin 2 YES exonic 639 3160684 −3 −3.9 Chromosome 9 open reading frame 68 YES exonic 640 4023355 −2 −3.87 Four and a half LIM domains 1 NO antisense 641 3862191 −3 −3.83 Fc fragment of IgG binding protein NO exonic 642 2997556 −3 −3.83 Engulfment and cell motility 1 NO antisense 643 3787882 −3 −3.79 Lipase, endothelial NO exonic 644 2483130 −2 −3.79 EGF-containing fibulin-like NO antisense extracellular matrix protein 1 645 3811426 −3 −3.78 B-cell CLL/lymphoma 2 NO intronic 646 3787871 −2 −3.78 Lipase, endothelial YES exonic 647 2660997 −3 −3.74 Inositol 1,4,5-triphosphate receptor, NO antisense type 1 648 3787872 −3 −3.72 Lipase, endothelial YES exonic 649 2763609 −3 −3.7 Peroxisome proliferator-activated YES exonic receptor gamma, coactivator 1 alpha 650 2980500 −3 −3.68 Phosphoinositide-binding protein NO intronic PIP3-E 651 2586129 −4 −3.66 Low density lipoprotein-related YES exonic protein 2 652 4023341 −3 −3.65 Four and a half LIM domains 1 NO antisense 653 2897466 −2 −3.64 Inhibitor of DNA binding 4, dominant NO extra- negative helix-loop-helix protein genic 654 3791755 −3 −3.63 B-cell CLL/lymphoma 2 NO antisense 655 3811429 −3 −3.62 B-cell CLL/lymphoma 2 NO intronic 656 2649081 −2 −3.59 Potassium voltage-gated channel, NO extra- shaker-related subfamily, beta genic member 1 657 2830194 −8.6 −7.06 Sparc/osteonectin, cwcv and kazal- NO antisense like domains proteoglycan (testican) 1 658 2830301 −1.2 −3.02 Sparc/osteonectin, cwcv and kazal- NO antisense like domains proteoglycan (testican) 1 659 2830378 1.8 4.94 Sparc/osteonectin, cwcv and kazal- NO antisense like domains proteoglycan (testican) 1

TABLE 4  Thyroid Nodule Disease 7-RNA Expression Signature Probeset Target SEQ Overlaps Sequence ID MFD Weights Gene CDS Subtype Probeset Sequence 1 14 8.75 High mobility NO intronic attctttggagttgcgtcattaggagctttacagt group aagatatcttactagccaatattagcctgccac AT-hook 2 agg 2 11 8.59 Cadherin 3, NO exonic gggcaacctaggcacactcagtataaaaac type 1, gcagagatccatccgaatgggaggcattggg P-cadherin gtctggaaaccagaaatgcaggacggccagt (placental) 3 3 8.12 Serpin YES exonic ctgcctctcgctcctcaacccctccc peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1 4 4 5.94 Insulin-like NO exonic tggagctgtcatcactcaacaaaaaccgag growth gccctcaatccaccttcaggccccgccccatg factor ggcccctcaccgctggttggaaagagtgttgg binding  tgttggctggggtgtcaataaagctgt protein 6 5 −14 −11.03 Thyroid NO antisense agcctgtgagtatcccggccttccatcccagc peroxidase actctcctgcaaaaatgcagaacaaacatta ataacgtctgaattctgactgctccagtaacag cacacacttgatacaagacagaagggcctg gacaggagctcttttcacaaaggaccgctggc gagatgagccatccttcttgcttgtcatgctgga gggaagtttctggcttccggacaaatgcttgttt gcaggaggccacagaggatccacgtggca aaggttcctactcacagagtgggatctgcaat ccgcaggcagggtccttcaggaagaagccgt gctgagcacacaggtccctgcgaccacagat gaacagaacgcctggccttgcccctggtgtca gtcagactaattaggggcccaggggacttgct ggcactgcccttgctgtggctggaaaagctatt taaattcacaaagagcctcacttccaggccag gtccttttggttccatgcgtgggagccaggtgc aaagacgaatgtgctctcagcagaacgaccc tgggctctgcttatctccgaggacaggatctaa tctcacactgaatttcagaagggagaccgatc atgttggcaatgcccccctcctgtatgcaagta agtgact 6 −5 −9.09 V-kit  NO exonic aagcccatgagtccttgaaaatattttttatatat Hardy-Zuckerman acagtaactttatgtgtaaatacataagcggcg 4 feline taagtttaaaggatgttggtgttccacgtgttttat sarcoma viral tcctgtatgttgtccaattgttgaca oncogene homolog 7 −31 −8.23 Metallophos- NO intronic tttttgcctgcacattctgagagagccatttggca phoesterase aacaacaataataaatttacatttggatggtgt domain atttcactcagagaactttcatatgtattatctcat containing ttggggctcacaacaattctttaaccagggtat 2 gtattactaataataattaacaatagccagcatt tacagtgtttatatgtcaactgctgttcattattgc ctccttttgacagagaccaaca

TABLE 5  Thyroid Nodule Disease 36-RNA Expression Signature Probeset Target Overlaps Sequence SEQ ID MFD Weights Gene CDS Subtype Probeset Sequence 1 14 8.75 High mobility NO intronic attctttggagttgcgtcattaggagcttta group AT-hook 2 cagtaagatatcttactagccaatattag cctgccacagg 2 11 8.59 Cadherin 3, NO exonic gggcaacctaggcacactcagtataaa type 1,  aacgcagagatccatccgaatgggag P-cadherin gcattggggtctggaaaccagaaatgc (placental) aggacggccagt 3 3 8.12 Serpin YES exonic ctgcctctcgctcctcaacccctccc peptidase inhibitor,  clade A (alpha-1 antiproteinase. antitrypsin), member 1 4 4 5.94 Insulin-like NO exonic tggagctgtcatcactcaacaaaaaacc growth factor gaggccctcaatccaccttcaggccccg binding ccccatgggcccctcaccgctggttgga protein 6 aagagtgttggtgttggctggggtgtcaa taaagctgt 5 −14 −11.03 Thyroid NO antisense agcctgtgagtatcccggccttccatccc peroxidase agcactctcctgcaaaaatgcagaaca aacattaataacgtctgaattctgactgct ccagtaacagcacacacttgatacaag acagaagggcctggacaggagctctttt cacaaaggaccgctggcgagatgagc catccttcttgcttgtcatgctggagggaa gtttctggcttccggacaaatgcttgtttgc aggaggccacagaggatccacgtggc aaaggttcctactcacagagtgggatct gcaatccgcaggcagggtccttcagga agaagccgtgctgagcacacaggtccc tgcgaccacagatgaacagaacgcctg gccttgcccctggtgtcagtcagactaatt aggggcccaggggacttgctggcactg cccttgctgtggctggaaaagctatttaa attcacaaagagcctcacttccaggcca ggtccttttggttccatgcgtgggagcca ggtgcaaagacgaatgtgctctcagca gaacgaccctgggctctgcttatctccga ggacaggatctaatctcacactgaatttc agaagggagaccgatcatgttggcaat gcccccctcctgtatgcaagtaagtgact 6 −5 −9.09 V-kit Hardy- NO exonic aagcccatgagtccttgaaaatattttttat Zukerman 4 atatacagtaactttatgtgtaaatacata feline sarcoma agcggcgtaagttttaaaggatgttggtgt viral oncogene tccacgtgttttattcctgtatgttgtccaatt homolog gttgaca 7 −31 −8.23 Metallophos- NO intronic ttttgcctgcacattctgagagagccattt phoesterase ggcaaacaacaataataaatttacatttg domain gatggtgtatttcactcagagaactttcat containing 2 atgtattatctcatttggggctcacaacaa ttctttaaccagggtatgtattactaataat aattaacaatagccagcatttacagtgttt atatgtcaactgctgttcattattgcctccttt tgacagagaccaaca 8 8 10.26 PDZ and LIM NO exonic tgctcccacgcctgcttcttaaggtccctg domain 4 ctcggccggtgtaaatatgtttcaccctgt ccctctaataaagctcctctgc 9 24 8.9 Tumor- NO exonic gacaacccgggatcgtttgcaagtaact associated gaatccattgcgacattgtgaaggcttaa calcium signal atgagtttagatgggaaatagcgttgttat transducer 2 cgccttgggtttaaattatttgatgagttcc acttgtatcatggcctacccgaggagaa gaggagtttgttaactgggcctatgtagta gcctcatttaccatcgtttgtattactgacc acatatgcttgtcactgggaaagaagcc tgtttcagctgcctgaacgcagtttggatg tctttgaggacagacattgcccggaaac tcagtctattta 10 6 8.69 Fibronectin 1 NO antisense acgcccaggactatttgaactgatgaaa tatccaaatgtccagtcctatagcctactt acacctaagtgaacactacagcgaagt gttcagtgaacagaccttgagaaggtct gtaattagatgataaggaagggtcacctt gccccatggcctctcgaagccctccattc cacctgtgagtgtgactcagctggatttc gagtgggatgaagttgctgcaacagctg gatatttctcgcatccacctgagagtatgt ctctggagggcacatgataatccttgctg tggtgctgtggattgccatttatgcctcac aacaaccctggaaggtagtttgctcctttt aaaagcaatttcttgtcatacaacgtact gcatatctttaaagtgatgggtttttacatat gtgtgacttgggagaccatcaccgcattc 11 13 8.66 Fibronectin 1 NO antisense cttcaaggacaaatcgtaaaggtagtgtt ttagacttctgcacacaaatggaaattca ggtagaatatctttcttttctagaatcatcta tcttactcaaaaagg 12 17 8.61 Fibronectin 1 NO intronic gtctgaatgcccacgacatgtcttttgcaa ttcacatagggaaagtgaacttgttggtt agtttatgtcttgagctgagccctttacga acatcttttttccttctcagtgccaagcgag gaatttacagagaaagaagttgtgaaa ccaccatagttagttgctgtgctttgaattt cttttgctcaaatggcctcagcgaaatctt atttgc 283 −8 −10.87 Thyroid YES exonic cgacaagtgtggcttcccagagagcgt peroxidase ggagaatggggactttgtgcactgtgag gagtctgggaggcgcgtgctggtgtattc ctgc 284 −6 −9.87 Thyroid NO antisense ggaggttgcacagattgcgggggatttg peroxidase gggaga 285 −3 −9.81 Hepatic YES exonic atggagaaaatgtcccgaccgctcccc leukemia ctgaatcccacctttatcccgcctccctac factor ggcgtgctcaggtccctg 286 −11 −9.45 Metallophos- NO extra-genic atggtggaggatggtagtttcgtcctggg phoesterase gaaggagggatttattcatatgcaacatc domain agttatgcctttcaga containing 2 287 −19 −9.37 Thyroid NO antisense tccaactttcacgagaagattaagaaaa peroxidase tccacaaacaatacctgttgttgcaagct cctgtgatgggcctgtatttgttcgccagg ca 288 −26 −9.14 Thyroid peroxidase YES exonic cgctattctgacctcctgatggcatgggg acaatacatcgaccacgacatcgcgttc acaccacagagcaccagcaaagctgc cttcgggggaggggctgactgccagat gacttgtgagaacca 289 −9 −9.07 Zinc finger, NO exonic cttattttacacatccgaagaaacaccat martin type 4 caca 290 −19 −8.91 Trefoil factor NO exonic agttcatatctggagcctgatgtcttaacg 3 (intestinal) aataaaggtcccatgctc 291 −3 −8.86 Peroxisome YES exonic aagacgtccctgctcggagcttctcaaat proliferator- atctgaccacaaacgatgaccctcctca activated caccaaacccacagagaacagaaac receptor agcagcagagacaaatgcacctccaa gamma, aaagaagtcccacacacagtcgcagtc coactivator 1 acaacacttacaag alpha 292 −30 −8.84 Thyroid NO extra-genic gtccctggaaggcaattaaggcgcccat peroxidase ttcagaagagttacagccgtgaaaatta ctcagc 293 −6 −8.84 Metallothionein YES exonic gctgtgtttgcaaaggggcgtcagagaag 1F 294 −55 −8.64 Thyroid YES exonic tgctgagcatcattgcaaacatgtctgga peroxidase tgtctcccttacatgctgcccccaaaatg cccaaacacttgcctggcgaacaaata caggcccatcacaggagcttgcaacaaca 295 −19 −8.52 Thyroid NO extra-genic ctcctgggaagagcactcctggcttcctg peroxidase cagggccggtgggaggaggaaagtga ttctgagggagaggctggagccttaagg accaacaaggcaaagtgacttgtctcat cctccagagattcaccaacacatgagc ctcagaccccaggcttctgcctccagca gccgccctgccgcacactgctcttactcc tccttataccctcactcacggggaacac agcccagtgatcccggaggaaactcac tccctccctgactcaacaaggcagtctc gggggcaccgttagccacgcgaccctg taaagctgccgtcctcatttcacatgtgaa gcagctgaattccagagtgctgggtccc agcccaggcagccctcagcctcacgca aggcaatagttaggagtccttcggcattg aaagcaaactcagacacatctgacctg gagttctacctgcactaagagaagaga gtggtaactaattcatggataaaacaga ccatcgaggcagcactgaatgatctcac ccacgaatgacaacagtggcacagga gggctatgaacattttgcttcaggatgtttt atttcgctctactgttatgtagagaaaagca tggtttgctttttataacttttgtgacccaaa aataccagactgtt 296 −14 −8.49 Metallophos- NO exonic gttagtgttcgccaaaggacagccaag phoesterase ctttcttttaaaaagtgataaaagtcttatttt domain aatatgctttaagctgaaagaaaaaaa containing 2 aataagaaacaggcagtgttttaaaac caacacagatttgcacaactgtttaaga gtattgtttgaaatattttaattttcaatgtttt gttgttgttgttttcttggtaatgcttcttttttgc agatgtggtcccaatttatagcaatcttct caacagaagtaggcatggaaaagactt cttttcatactctcactataaagaaagctg cattgagaagaaaatggctgtcatttaaa ggatggtttaactagtgagattcctattgtg gttatacaaggtctcattgtttgtttgtttctttt aaattatttcagctttaaaaatacagaaat ggaatctgtcaagagcaggtatttcatac ggttaaaaaaatgaacatgcagactcct tttcaatatgggtttatatatataagtatttttt gtgtattatgactacgttaggagtttattatt gtcaaggacagtacaactgcaaaggg atgctgtatagcaacacatcagaagtcg gaaggaactgacacattctctcagagct caaggtcttaaagagcttgagttaaatct aggtacagttacaggcatgtatagactta aatggatgcaatggaagctaactaaaat aaggcttagttgtcctttctatttaaatacc ccaagttgtcttcttacttcctctcccctctc ccattttgcactgtgtgtcgatgcaatcttcg 297 −24 −8.39 Metallophos- NO intronic gttctgcattgctgataccgctagtggtttt phoesterase aaaaatagaaatcaaaataagaaccct domain gatattaaggattcacag containing 2 298 −5 −8.37 Thyroid NO intronic cccaggccacacaagactcacggctct peroxidase ccctggtctc 299 −9 −8.35 Metallophos- NO intronic gagctacccagtctacgctattttgttaat phoesterase agcatctcaaacagcccaaggcaggta domain ggcagggaatataatgggaagatgaat containing 2 tttatagagggaacaagaggagaaatg ggcgtatttgtgaaggagagagggaaa aagtaggagggaatatatagcagatgt gtttgtgagatcataactcttccttgtcagtt acgatgtcctgaccttgggcttgactttag caccgggagcaggtcagcatccctaga cttcagtcaacagggagatg 300 −8 −8.35 Solute carrier YES exonic family 26, member 4 304 −14 −8.22 Metallothionein NO exonic ccctgctcccaagtacaaatagagtgac 1G ccgtaaaatccaggat 305 −6 −8.21 Solute carrier YES exonic accttttccagtggtgagtttaatggtggg family 26, atctgttgttctgagcatggcccccgacg member 4 aacactttctcgtatccagcagcaatgga actgtattaaatactactatgatagacact gcagctagagatacagctagagtcctg attgccagtgccctgactctgctggttg 306 −8 −8.16 Solute carrier YES exonic tggtggcttgcagattggattcatagtgag family 26, gtacttggcagatcctttggttggtggcttc member 4 acaacagctgctgccttccaagtgctggt ctcacagctaaagattgtc

TABLE 6 7-RNA and 36-RNA POP scores for each of the samples evaluated in the three subsets of patient specimens Specimen Cytology Pathology Final Review 7-RNA 36-RNA Number Study Diagnosis Diagnosis Histology POP Score POP Score 1 Training Benign Benign Goiter 21.9 0.0 Subset 2 Training Benign Benign Goiter 15.9 4.1 Subset 3 Training Benign Benign Goiter 28.4 5.8 Subset 4 Training Benign Benign Goiter 22.4 7.6 Subset 5 Training Benign Benign Goiter 15.9 7.8 Subset 6 Training Benign Benign Goiter 25.7 7.8 Subset 7 Training Benign Benign Goiter 12.4 8.1 Subset 8 Training Benign Benign Goiter 19.9 10.8 Subset 9 Training Benign Benign Goiter 34.2 11.6 Subset 10 Training Benign Benign Goiter 27.5 11.9 Subset 11 Training Benign Benign Goiter 27.3 13.1 Subset 12 Training Benign Benign Goiter 31.7 15.1 Subset 13 Training Benign Benign Goiter 30.4 18.9 Subset 14 Training Benign Benign Goiter 28.6 21.5 Subset 15 Training Benign Benign Hashimoto's 28.3 26.7 Subset 16 Training Suspicious Benign Follicular 34.3 16.9 Subset for Cancer Adenoma 17 Training Suspicious Benign Follicular 37.6 21.8 Subset for Cancer Adenoma 18 Training Suspicious Benign Follicular 31.5 21.8 Subset for Cancer Adenoma 19 Training Suspicious Benign Follicular 52.9 40.4 Subset for Cancer Adenoma 20 Training Suspicious Benign Goiter 17.0 13.1 Subset for Cancer 21 Training Suspicious Benign Hurthle Cell 22.8 3.5 Subset for Cancer Adenoma 22 Training Suspicious Benign Hurthle Cell 28.2 11.3 Subset for Cancer Adenoma 23 Training Suspicious Benign Hurthle Cell 30.8 16.6 Subset for Cancer Adenoma 24 Training Suspicious Benign Hurthle Cell 31.6 19.8 Subset for Cancer Adenoma 25 Training Suspicious Benign Thyroiditis 25.0 11.3 Subset for Cancer 26 Training Cancer Malignant Papillary 73.9 63.1 Subset Carcinoma 27 Training Cancer Malignant Papillary 64.5 63.1 Subset Carcinoma 28 Training Cancer Malignant Papillary 72.0 71.8 Subset Carcinoma 29 Training Cancer Malignant Papillary 82.5 77.6 Subset Carcinoma 30 Training Cancer Malignant Papillary 78.7 79.4 Subset Carcinoma 31 Training Cancer Malignant Papillary 84.7 84.6 Subset Carcinoma 32 Training Cancer Malignant Papillary 86.6 90.1 Subset Carcinoma 33 Training Cancer Malignant Papillary 100.0 91.3 Subset Carcinoma 34 Training Cancer Malignant Papillary 83.7 94.8 Subset Carcinoma 35 Training Cancer Malignant Papillary 85.6 94.8 Subset Carcinoma 36 Training Cancer Malignant Papillary 91.7 95.9 Subset Carcinoma 37 Training Cancer Malignant Papillary 91.7 96.5 Subset Carcinoma 38 Training Cancer Malignant Papillary 86.0 100.0 Subset Carcinoma 39 Training Suspicious Malignant Follicular 52.7 34.9 Subset for Cancer Carcinoma 40 Training Suspicious Malignant Follicular 58.0 38.1 Subset for Cancer Carcinoma 41 Training Suspicious Malignant Follicular 68.4 69.2 Subset for Cancer Carcinoma 42 Training Suspicious Malignant Papillary 32.2 20.9 Subset for Cancer Carcinoma 43 Training Suspicious Malignant Papillary 77.7 69.5 Subset for Cancer Carcinoma 44 Training Suspicious Malignant Papillary 88.3 77.0 Subset for Cancer Carcinoma 45 Training Suspicious Malignant Papillary 73.4 79.1 Subset for Cancer Carcinoma 46 Training Suspicious Malignant Papillary 87.2 89.2 Subset for Cancer Carcinoma 47 Training Suspicious Malignant Papillary 87.5 93.0 Subset for Cancer Carcinoma 48 Training Suspicious Malignant Papillary 85.9 96.5 Subset for Cancer Carcinoma 49 Follicular Suspicious Benign Follicular 26.5 12.5 Subset for Cancer Adenoma 50 Follicular Suspicious Benign Follicular 21.7 13.1 Subset for Cancer Adenoma 51 Follicular Suspicious Benign Follicular 19.0 14.8 Subset for Cancer Adenoma 52 Follicular Suspicious Benign Follicular 32.0 17.7 Subset for Cancer Adenoma 53 Follicular Suspicious Benign Follicular 34.3 22.1 Subset for Cancer Adenoma 54 Follicular Suspicious Malignant Follicular 0.0 1.5 Subset for Cancer Carcinoma 55 Follicular Suspicious Malignant Follicular 53.1 41.0 Subset for Cancer Carcinoma 56 Follicular Suspicious Malignant Follicular 75.5 65.1 Subset for Cancer Carcinoma 57 Follicular Suspicious Malignant Papillary 43.3 32.8 Subset for Cancer Carcinoma 58 Follicular Suspicious Malignant Papillary 39.9 39.5 Subset for Cancer Carcinoma 59 Preoperative Benign Benign Goiter 30.7 19.2 Cell Block Subset 60 Preoperative Benign Benign Goiter 22.0 20.1 Cell Block Subset 61 Preoperative Suspicious Benign Hurthle Cell 26.8 13.7 Cell Block for Cancer Adenoma Subset 62 Preoperative Cancer Malignant Papillary 77.9 66.0 Cell Block Carcinoma Subset 63 Preoperative Cancer Malignant Papillary 69.4 75.0 Cell Block Carcinoma Subset 64 Preoperative Cancer Malignant Papillary 91.5 89.5 Cell Block Carcinoma Subset 65 Preoperative Cancer Malignant Papillary 71.5 90.4 Cell Block Carcinoma Subset 66 Preoperative Cancer Malignant Papillary 92.3 92.4 Cell Block Carcinoma Subset

TABLE 7  Thyroid Nodule Disease SPOCK1-RNA Expression Signature Probe Location Probeset Target SEQ rel. to Overlaps Sequence Probeset ID ID MFD Weights Gene Gene CDS Subtype Sequence 261 2876911 2.0 4.24 In EXON Sparc/osteonectin, YES exonic ctgagggtgcacac #11 cwcv and kazal-like ccgagccgtgacag domains agg proteoglycan (testican) 1 657 2830194 −8.6 −7.06 In Sparc/osteonectin, NO antisense atgaactccctcgtg INTRON  cwcv and kazal-like gcggccgaaggcct #6 domains gg proteoglycan (testican) 1 658 2830301 −1.2 −3.02 In Sparc/osteonectin, NO antisense ttatttcgtgtgtgtct INTRON cwcv and kazal-like cttggctctgtag #3 domains proteoglycan (testican) 1 659 2830378 1.8 4.94 In Sparc/osteonectin, NO antisense tgcttgcacccaca INTRON cwcv and kazal-like gtccatagacatcc #2 domains aaccgcccaatgca proteoglycan tatccaacctcagc (testican) 1 aactttacaatcccc aacgccagcccac 

1.-35. (canceled)
 36. A method, comprising: (a) assaying by sequencing, array hybridization or nucleic acid amplification the expression level of one or more transcripts in a test sample from a thyroid nodule of a subject, wherein said one or more transcripts includes at least one transcript corresponding to at least one sequence selected from SEQ ID No.1-12, 261, 283-306, 657, 658, and 659; and (b) in a programmed computer, comparing said expression level of each of said one or more transcripts with reference expression levels of transcripts corresponding to sequences as set forth in SEQ ID No.1-12, 261, 283-306, 657, 658, and 659, to classify said thyroid nodule as malignant or benign.
 37. The method of claim 1, wherein an increased relative level of expression of said one or more transcripts, a decreased relative level of expression of said one or more transcripts, or a combination thereof is used to classify the thyroid nodule as malignant.
 38. The method of claim 1, wherein an increased relative level of expression of said one or more transcripts, a decreased relative level of expression of said one or more transcripts, or a combination thereof is used to classify the thyroid nodule as benign.
 39. The method of claim 1, wherein said assaying comprises determining the expression level by RT-PCR, Northern blotting, ligase chain reaction, or a combination thereof.
 40. The method of claim 1, further comprising measuring the expression level of at least one control nucleic acid in the test sample.
 41. The method of claim 1, wherein the test sample is fresh-frozen or fixed.
 42. The method of claim 1, wherein the expression level is measured by pattern recognition.
 43. The method of claim 1, wherein (b) comprises using said programmed computer to (1) classify said thyroid nodule as malignant if there is (i) an increase in an expression level corresponding to at least one transcript corresponding to at least one sequence selected from SEQ ID No.1-4, 8-12, and 261, or (ii) a decrease in an expression level corresponding to at least one transcript corresponding to at least one sequence selected from SEQ ID No. 5-7, 283-306, 657, 658, and 659, or (2) classify said thyroid nodule as benign if there is (i) an increase in said expression level corresponding to at least one sequence selected from SEQ ID No. 5-7, 283-306, 657, 658, and 659 or (ii) a decrease in said expression level corresponding to at least one sequence selected from SEQ ID No.1-4, 8-12, and
 261. 44. The method of claim 1, wherein said assaying is by nucleic acid amplification using at least two primers that each amplifies a transcript corresponding to a sequence as set forth in any one of SEQ ID No.1-12, 261, 283-306, 657, 658, and
 659. 45. The method of claim 1, further comprising (c) based upon a classification of said thyroid nodule as malignant or benign, (i) designating a treatment modality for said subject or (ii) generating a report that designates said thyroid nodule as malignant or benign.
 46. The method of claim 10, wherein said treatment modality is selected from total thyroidectomy, near-total thyroidectomy, partial thyroidectomy, cosmetic debulking, radioactive iodine treatment, watchful waiting, thyroid hormone suppression therapy, total or near-total thyroidectomy followed by radioactive iodine ablation therapy and permanent thyroid hormone replacement therapy, or a combination thereof. 